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Dissertations / Theses on the topic 'Industrial machine vision'
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Field, Matthew. "Machine vision system developments for industrial inspection applications." Thesis, University of Central Lancashire, 1997. http://clok.uclan.ac.uk/20334/.
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This thesis describes research in the area of automated industrial inspection using machine vision systems. It is anticipated that the algorithms described will contribute to the design of a machine vision system for the automatic surface inspection of cylindrical pellets. Firstly, the acquisition and segmentation of pellet tray images using area capture is described. Individual pellets are segmented from a pellet tray image by a novel system using the Radon transform coupled with data clustering. Subsequent to the segmentation, the linking of four pellet views depicting the entire circumferential area of the pellet is described along with a simple technique to compensate for intensity variations brought about by imaging the three-dimensional cylindrical surface of the pellet. The image processing techniques of filtering, edge detection, thresholding and morphology are used in the segmentation of grey level pellet defect images. The grey level pellet images are low-pass filtered and binary images formed using edge detection with thresholding. Binary morphology operators are then used in conjunction with a termination condition based on the number of objects in the image to ensure homogenous defect representations. The problem of overlapping defects is addressed, resulting in a second algorithm using the Radon transform coupled with data clustering. Prior to classification salient features are extracted from a set of synthetic binary defect images to form feature vectors. The novel idea of image object classification using 100% fuzzy inference is described, and results are shown to be superior to results obtained by feature space classifiers. The sub-classification of crack defects is carried out using a heuristic classifier, and the parameterisation of pellet defects is described.
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Cho, Tai-Hoon. "A knowledge-based machine vision system for automated industrial web inspection." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-134615/.
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Leung, Tin Wah William. "High precision camera-based colour inspection of industrial products." Thesis, University of Huddersfield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338605.
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King, William E. "Using an FPGA-Based Processing Platform in an Industrial Machine Vision System." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/31799.
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This thesis describes the development of a commercial machine vision system as a case study for utilizing the Modular Reprogrammable Real-time Processing Hardware (MORRPH) board. The commercial system described in this thesis is based on a prototype system that was developed as a test-bed for developing the necessary concepts and algorithms. The prototype system utilized color linescan cameras, custom framegrabbers, and standard PCs to color-sort red oak parts (staves). When a furniture manufacturer is building a panel, very often they come from edge-glued paneled parts. These are panels formed by gluing several smaller staves together along their edges to form a larger panel. The value of the panel is very much dependent upon the â matchâ of the individual stavesâ i.e. how well they create the illusion that the panel came from a single board as opposed to several staves.
The prototype system was able to accurately classify staves based on color into classes defined through a training process. Based on Trichromatic Color Theory, the system developed a probability density function in 3-D color space for each class based on the parts assigned to that class during training. While sorting, the probability density function was generated for each scanned piece, and compared with each of the class probability density functions. The piece was labeled the name of the class whose probability density function it most closely matched. A â best-faceâ algorithm was also developed to arbitrate between pieces whose top and bottom faces did not fall into the same classes. [1] describes the prototype system in much greater detail.
In developing a commercial-quality machine vision system based on the prototype, the primary goal was to improve throughput. A Field Programmable Gate Array (FPGA)-based Custom Computing Machine (FCCM) called the MORRPH was selected to assume most of the computational burden, and increase throughput in the commercial system. The MORRPH was implemented as an ISA-bus interface card, with a 3 x 2 array of Processing Elements (PE). Each PE consists of an open socket which can be populated with a Xilinx 4000 series FPGA, and an open support socket which can be populated with support chips such as external RAM, math processors, etc.
In implementing the prototype algorithms for the commercial system, a partition was created between those algorithms that would be implemented on the MORRPH board, and those that would be left as implemented on the host PC. It was decided to implement such algorithms as Field-Of-View operators, Shade Correction, Background Extraction, Gray-Scale Channel Generation, and Histogram Generation on the MORRPH board, and to leave the remainder of the classification algorithms on the host.
By utilizing the MORRPH board, an industrial machine vision system was developed that has exceeded customer expectations for both accuracy and throughput. Additionally, the color-sorter received the International Woodworking Fairâ s Challengers Award for outstanding innovation.Master of Science
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Mosberger, Rafael. "Vision-based Human Detection from Mobile Machinery in Industrial Environments." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-48324.
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The problem addressed in this thesis is the detection, localisation and tracking of human workers from mobile industrial machinery using a customised vision system developed at Örebro University. Coined the RefleX Vision System, its hardware configuration and computer vision algorithms were specifically designed for real-world industrial scenarios where workers are required to wear protective high-visibility garments with retro-reflective markers. The demand for robust industry-purpose human sensing methods originates from the fact that many industrial environments represent work spaces that are shared between humans and mobile machinery. Typical examples of such environments include construction sites, surface and underground mines, storage yards and warehouses. Here, accidents involving mobile equipment and human workers frequently result in serious injuries and fatalities. Robust sensor-based detection of humans in the surrounding of mobile equipment is therefore an active research topic and represents a crucial requirement for safe vehicle operation and accident prevention in increasingly automated production sites. Addressing the described safety issue, this thesis presents a collection of papers which introduce, analyse and evaluate a novel vision-based method for detecting humans equipped with protective high-visibility garments in the neighbourhood of manned or unmanned industrial vehicles. The thesis provides a comprehensive discussion of the numerous aspects regarding the design of the hardware and the computer vision algorithms that constitute the vision system. An active nearinfrared camera setup that is customised for the robust perception of retroreflective markers builds the basis for the sensing method. Using its specific input, a set of computer vision and machine learning algorithms then perform extraction, analysis, classification and localisation of the observed reflective patterns, and eventually detection and tracking of workers with protective garments. Multiple real-world challenges, which existing methods frequently struggle to cope with, are discussed throughout the thesis, including varying ambient lighting conditions and human body pose variation. The presented work has been carried out with a strong focus on industrial applicability, and therefore includes an extensive experimental evaluation in a number of different real-world indoor and outdoor work environments.
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Koslav, Maria B. "Development of a machine vision based oyster meat sorter." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/53225.
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Oyster meats are currently sorted by hand using volume as the sorting parameter. Hand grading is inaccurate, time consuming and costly. Previous research on physical properties of oyster meats showed a high correlation between projected area of oyster meats and their volume thus allowing the use of projected area measurements as a sorting criterion. A machine vision based oyster meat sorting machine was developed to mechanize the sorting process. The machine consists of a dark conveyor belt transporting singulated oysters through a grading station and then along a row of fast acting water jet valves which separates the stream of oysters into 3 classes. The vision system consists of a monochrome television camera, flash light illumination to "freeze" the images, a digitizer/transmitter and a Personal Computer as an image processing unit. Software synchronizes the flash light and digitization of images and calculates projected area of each meat using the planimeter method. The grading results are sent to a valve control board which actuates the spray valves. The sorting rate is 37 oyster meats/min with a sorting accuracy of 87.5%. A description of the design work, adjustment and l calibration procedures and a final sorting test is included.Master of Science
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Zhang, Zhengwen. "Self-learning systems and neural networks for image texture analysis." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296217.
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Parker, Johne' Michelle. "An analytical and experimental investigation of physically-accurate synthetic images for machine vision design." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/19038.
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Parvez, Bilal. "Embedded Vision Machine Learning on Embedded Devices for Image classification in Industrial Internet of things." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219622.
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Because of Machine Learning, machines have become extremely good at image classification in near real time. With using significant training data, powerful machines can be trained to recognize images as good as any human would. Till now the norm has been to have pictures sent to a server and have the server recognize them. With increasing number of sensors the trend is moving towards edge computing to curb the increasing rate of data transfer and communication bottlenecks. The idea is to do the processing locally or as close to the sensor as possible and then only transmit actionable data to the server. While, this does solve plethora of communication problems, specially in industrial settings, it creates a new problem. The sensors need to do this computationally intensive image classification which is a challenge for embedded/wearable devices, due to their resource constrained nature. This thesis analyzes Machine Learning algorithms and libraries from the motivation of porting image classifiers to embedded devices. This includes, comparing different supervised Machine Learning approaches to image classification and figuring out which are most suited for being ported to embedded devices. Taking a step forward in making the process of testing and implementing Machine Learning algorithms as easy as their desktop counterparts. The goal is to ease the process of porting new image recognition and classification algorithms on a host of different embedded devices and to provide motivations behind design decisions. The final proposal goes through all design considerations and implements a prototype that is hardware independent. Which can be used as a reference for designing and then later porting of Machine Learning classifiers to embedded devices.Maskiner har blivit extremt bra på bildklassificering i nära realtid. På grund av maskininlärning med kraftig träningsdata, kan kraftfulla maskiner utbildas för att känna igen bilder så bra som alla människor skulle. Hittills har trenden varit att få bilderna skickade till en server och sedan få servern att känna igen bilderna. Men eftersom sensorerna ökar i antal, går trenden mot så kallad "edge computing" för att stryka den ökande graden av dataöverföring och kommunikationsflaskhalsar. Tanken är att göra bearbetningen lokalt eller så nära sensorn som möjligt och sedan bara överföra aktiv data till servern. Samtidigt som detta löser överflöd av kommunikationsproblem, speciellt i industriella inställningar, skapar det ett nytt problem. Sensorerna måste kunna göra denna beräkningsintensiva bildklassificering ombord vilket speciellt är en utmaning för inbyggda system och bärbara enheter, på grund av sin resursbegränsade natur. Denna avhandling analyserar maskininlärningsalgoritmer och biblioteken från motivationen att portera generiska bildklassificatorer till inbyggda system. Att jämföra olika övervakade maskininlärningsmetoder för bildklassificering, utreda vilka som är mest lämpade för att bli porterade till inbyggda system, för att göra processen att testa och implementera maskininlärningsalgoritmer lika enkelt som sina skrivbordsmodeller. Målet är att underlätta processen för att portera nya bildigenkännings och klassificeringsalgoritmer på en mängd olika inbyggda system och att ge motivation bakom designbeslut som tagits och för att beskriva det snabbaste sättet att skapa en prototyp med "embedded vision design". Det slutliga förslaget går igenom all hänsyn till konstruktion och implementerar en prototyp som är maskinvaruoberoende och kan användas för snabb framtagning av prototyper och sedan senare överföring av maskininlärningsklassificatorer till inbyggda system.
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English, Jonathan. "Machine vision for the determination of identity, orientation and position of two dimensional industrial components." Thesis, De Montfort University, 1996. http://hdl.handle.net/2086/4811.
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Megahed, Fadel Mounir. "Towards the Utilization of Machine Vision Systems as an Integral Component of Industrial Quality Monitoring Systems." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/36243.
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Recent research discussed the development of image processing tools as a part of the quality control framework in manufacturing environments. This research could be divided into two image-based fault detection approaches: 1) MVS; and 2) MVS and control charts. Despite the intensive research in both groups, there is a disconnect between research and the actual needs on the shop-floor. This disconnect is mainly attributed to the following:
â ¢ The literature for the first category has mainly focused on improving fault detection accuracy through the use of special setups without considering its impact on the manufacturing process. Therefore, many of these methods have not been utilized by industry, and these tools lack the capability of using images already present on the shop floor.
â ¢ The studies presented on the second category have been mainly developed in isolation. In addition, most of these studies have focused more on introducing the concept of utilizing control charts on image data rather than tackling specific industry problems.
In this thesis, these limitations are investigated and are disseminated to the research community through two different journal papers. In the first paper, it was shown that a face-recognition tool could be successfully used to detect faults in real-time in stamped processes, where the changes in image lighting conditions and part location were allowed to emulate actual manufacturing environments. On the other hand, the second paper reviewed the literature on image-based control charts and suggested recommendations for future research.Master of Science
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Belgiovine, Mauro. "Advanced industrial OCR using Autoencoders." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13807/.
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Il contenuto di questa tesi di laurea descrive il lavoro svolto durante un tirocinio di sei mesi presso Datalogic ADC. L'obiettivo del lavoro è stato quello di utilizzare uno specifico tipo di rete neurale, chiamata Autoencoder, per scopi legati al riconoscimento o alla convalida di caratteri in un sistema OCR industriale. In primo luogo è stato creato un classificatore di immagini di caratteri basato su Denoising Autoencoder; successivamente, è stato studiato un metodo per utilizzare l'Autoencoder come un classificatore di secondo livello, per meglio distinguere le false attivazioni da quelle corrette in condizioni di incertezza di un classificatore generico. Entrambe le architetture sono state valutate su dataset reali di clienti di Datalogic e i risultati sperimentali ottenuti sono presentati in questa tesi.
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Straumann, Hugo M. "The development of a software package for low cost machine vision system for real time applications." Ohio : Ohio University, 1986. http://www.ohiolink.edu/etd/view.cgi?ohiou1183378665.
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Corni, Gabriele. "A study on the applicability of Long Short-Term Memory networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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This thesis summarises the research-oriented study of applicability of Long Short-Term Memory Recurrent Neural Networks (LSTMs) to industrial Optical Character Recognition (OCR) problems.
Traditionally solved through Convolutional Neural Network-based approaches (CNNs), the reported work aims to detect the OCR aspects that could be improved by exploiting recurrent patterns among pixel intensities, and speed up the overall character detection process.
Accuracy, speed and complexity act as the main key performance indicators.
After studying the core Deep Learning foundations, the best training technique to fit this problem first, and the best parametrisation next, have been selected. A set of tests eventually validated the preciseness of this solution.
The final results highlight how difficult is to perform better than CNNs for what OCR tasks are concerned. Nonetheless, with favourable background conditions, the proposed LSTM-based approach is capable of reaching a comparable accuracy rate in (potentially) less time.
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Nilsson, Jim, and Peter Valtersson. "Machine Vision Inspection of the Lapping Process in the Production of Mass Impregnated High Voltage Cables." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16707.
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Background. Mass impregnated high voltage cables are used in, for example, submarine electric power transmission. One of the production steps of such cables is the lapping process in which several hundred layers of special purpose paper are wrapped around the conductor of the cable. It is important for the mechanical and electrical properties of the finished cable that the paper is applied correctly, however there currently exists no reliable way of continuously ensuring that the paper is applied correctly. Objective. The objective of this thesis is to develop a prototype of a cost-effective machine vision system which monitors the lapping process and detects and records any errors that may occur during the process; with an accuracy of at least one tenth of a millimetre. Methods. The requirements of the system are specified and suitable hardware is identified. Using a method where the images are projected down to one axis as well as other signal processing methods, the errors are measured. Experiments are performed where the accuracy and performance of the system is tested in a controlled environment. Results. The results show that the system is able to detect and measure errors accurately down to one tenth of a millimetre while operating at a frame rate of 40 frames per second. The hardware cost of the system is less than €200. Conclusions. A cost-effective machine vision system capable of performing measurements accurate down to one tenth of a millimetre can be implemented using the inexpensive Raspberry Pi 3 and Raspberry Pi Camera Module V2. Th
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Rexhaj, Kastriot. "Machine visual feedback through CNN detectors : Mobile object detection for industrial application." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36467.
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This paper concerns itself with object detection as a possible solution to Valmet’s quest for a visual-feedback system that can help operators and other personnel to more easily interact with their machines and equipment. New advancements in deep learning, specifically CNN models, have been exploring neural networks with detection-capabilities. Object detection has historically been mostly inaccessible to the industry due the complex solutions involving various tricky image processing algorithms. In that regard, deep learning offers a more easily accessible way to create scalable object detection solutions. This study has therefore chosen to review recent literature detailing detection models with a selective focus on factors making them realizable on ARM hardware and in turn mobile devices like phones. An attempt was made to single out the most lightweight and hardware efficient model and implement it as a prototype in order to help Valmet in their decision process around future object detection products. The survey led to the choice of a SSD-MobileNetsV2 detection architecture due to promising characteristics making it suitable for performance-constrained smartphones. This CNN model was implemented on Valmet’s phone of choice, Samsung Galaxy S8, and it successfully achieved object detection functionality. Evaluation shows a mean average precision of 60 % in detecting objects and a 4.7 FPS performance on the chosen phone model. TensorFlow was used for developing, training and evaluating the model. The report concludes with recommending Valmet to pursue solutions built on-top of these kinds of models and further wishes to express an optimistic outlook on this type of technology for the future. Realizing performance of this magnitude on a mid-tier phone using deep learning (which historically is very computationally intensive) sets us up for great strides with this type of technology in the future; and along with better smartphones, great benefits are expected to both industry and consumers.Den här rapporten behandlar objekt detektering som en möjlig lösning på Valmets efterfrågan av ett visuellt återkopplingssystem som kan hjälpa operatörer och annan personal att lättare interagera med maskiner och utrustning. Nya framsteg inom djupinlärning har dem senaste åren möjliggjort framtagande av neurala nätverksarkitekturer med detekteringsförmågor. Då industrisektorn svårare tar till sig högst specialiserade algoritmer och komplexa bildbehandlingsmetoder (som tidigare varit fallet med objekt detektering) så ger djupinlärningsmetoder istället upphov till att skapa självlärande system som är återanpassningsbara och närmast intuitiva i dem fall där sådan teknologi åberopas. Den här studien har därför valt att studera ett par sådana teknologier för att hitta möjliga implementeringar som kan realiseras på något så enkelt som en mobiltelefon. Urvalet har därför bestått i att hitta detekteringsmodeller som är hårdvarumässigt resurssnåla och implementera ett sådant system för att agera prototyp och underlag till Valmets vidare diskussioner kring objekt-detekteringsslösningar. Studien valde att implementera en SSD-MobileNetsV2 modellarkitektur då den uppvisade lovande egenskaper kring hårdvarukraven. Modellen implementerades och utvärderades på Valmets mest förekommande telefon Samsung Galaxy S8 och resultatet visade på en god förmåga för modellen att detektera objekt. Den valda modellen gav 60 % precision på utvärderingsbilderna och lyckades nå 4.7 FPS på den implementerade telefonen. TensorFlow användes för programmering och som stödjande mjukvaruverktyg för träning, utvärdering samt vidare implementering. Studien påpekar optimistiska förväntningar av denna typ av teknologi; kombinerat med bättre smarttelefoner i framtiden kan det leda till revolutionerande lösningar för både industri och konsumenter.
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Miller, Michael E. "The development of an improved low cost machine vision system for robotic guidance and manipulation of randomly oriented, straight edged objects." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182445639.
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Popovský, Pavel. "Návrh kamerového systému na platformě VC5 a Vision Designer." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-376998.
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For the purpose of an future machine vision system development in Tyco Electronics Czech s.r.o. I have developed Cognex Designer template. Template will serve as a flexible basis for further development of camera applications on the Cognex VC5 industrial computer. The functionality of the program template has been successfully verified by modifying it to a particular application of the laboratory manual station used to measure the parameters of the manufactured connectors. A camera with lens and lightning was chosen and installed on the station. DIO communication was put into operation between VC5 and PLC system. The application has been calibrated and verified as a measurement system using MSA Type I and Capability study standard methods.
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SARKAR, SAURABH. "PATH PLANNING AND OBSTACLE AVOIDANCE IN MOBILE ROBOTS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196055597.
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Holmsten, Jenny. "Future Forests - Vision 2030 : Bachelor Thesis Report - Jenny Holmsten." Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-106634.
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Forests cover about 30% of the earth surface and is a vital resource as a habitat for plants, animals and humans. Today climate change and global warming is a fact and something must be done. We burn massive amounts of fossil fuels and during this combustion carbon dioxide is created. To help eliminate this global change we need to start caring about the forests. The forests have a major role in climate change and global warming. It currently contributes to about one-sixth of the global carbon emissions. But today deforestation is a real environmental threat. The world trees are being cut down too quickly for the earth to regenerate new forests. And while the society is moving into a more bio-based economy the pressure of a efficient forest industry and forest regrowth is increasing drastically. A new way of reforestation must happen, a sustainable and natural method must be im plemented. In Sweden and the Scandinavian area the method has had a stagnant development. Is done manually with a standardized procedure not taking natural properties into account. Money often goes over quality. My project will focus on developing a concept that can live up to the upcoming future demands and the environmental aspects that needs to be taken in account to ensure a healthy and sustainable forest. The final result performs an efficient and precise reforestation and enables for a detailed planning and analysis of the area in advanced.
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Moussallik, Laila. "Towards Condition-Based Maintenance of Catenary wires using computer vision : Deep Learning applications on eMaintenance & Industrial AI for railway industry." Thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83123.
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Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required. Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences. Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections. In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).
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Selingerová, Simona. "Systémy průmyslového vidění s roboty Kuka a jeho aplikace na synchronizaci pohybu robotu s pohybujícím se prvkem." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-229178.
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This diploma thesis deals with a practical application employing an industrial robot KUKA, a vision system – smart camera Siemens. The application is focused on synchronizing or robot movements with objects moving on a conveyor belt. The introductory and theoretical part of this thesis is concerned with various systems for machine vision currently available on the market. Practical part is then focused on the demonstration application: setting-up the robotic cell and description of all devices, robot and vision system programming.
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Albertazzi, Riccardo. "A study on the application of generative adversarial networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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High performance and nearly perfect accuracy are the standards required by OCR algorithms for industrial applications. In the last years research on Deep Learning has proven that Convolutional Neural Networks (CNNs) are a very powerful and robust tool for image analysis and classification; when applied to OCR tasks, CNNs are able to perform much better than previously adopted techniques and reach easily 99% accuracy.
However, Deep Learning models' effectiveness relies on the quality of the data used to train them; this can become a problem since OCR tools can run for months without interruption, and during this period unpredictable variations (printer errors, background modifications, light conditions) could affect the accuracy of the trained system. We cannot expect that the final user who trains the tool will take thousands of training pictures under different conditions until all imaginable variations have been captured; we then have to be able to generate these variations programmatically.
Generative Adversarial Networks (GANs) are a recent breakthrough in machine learning; these networks are able to learn the distribution of the input data and therefore generate realistic samples belonging to that distribution. This thesis' objective is learning how GANs work in detail and perform experiments on generative models that allow to create unseen variations of OCR training characters, thus allowing the whole OCR system to be more robust to future character variations.
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Nagy, Marek. "Synchronizace pohybu průmyslového robotu s pohybem pásového dopravníku." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231322.
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Diploma thesis is focused on the solution of synchronization of the robot motion with a moving conveyor belt. It informs about basic principles and possibilities of using similar applications. It describes individual elements used in the application, their importance and function. It provides an overview of proposed program codes for the programmable logic controller, the smart camera and the robot. The result is the creation of a functional illustrative application with KUKA robot.
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Grepl, Pavel. "Strojové vidění pro navádění robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443727.
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Master's thesis deals with the design, assembly, and testing of a camera system for localization of randomly placed and oriented objects on a conveyor belt with the purpose of guiding a robot on those objects. The theoretical part is focused on research in individual components making a camera system and on the field of 2D and 3D localization of objects. The practical part consists of two possible arrangements of the camera system, solution of the chosen arrangement, creating testing images, programming the algorithm for image processing, creating HMI, and testing the complete system.
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Poleto, Arthur Suzini. "Desenvolvimento de um sistema de visão de máquina para inspeção de conformidade em um produto industrial /." Ilha Solteira, 2019. http://hdl.handle.net/11449/191137.
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Orientador: João Antonio PereiraResumo: Visão de máquina é um campo multidisciplinar que vem crescendo na indústria, que está cada vez mais preocupada em reduzir custos, automatizar processos, e atender requisitos de qualidade do produto para atender seus clientes. Processos de montagem realizados de forma manual com inspeção e controle visual são tipicamente processos susceptíveis a erros, à utilização de peças não conformes na montagem do produto final. Este trabalho apresenta uma proposta de desenvolvimento de um sistema de visão de máquina com base no processamento e análise de imagens digitais para a inspeção das características e especificações das peças e componentes utilizados na montagem de capotas marítimas, objetivando verificar e garantir a conformidade do produto final. A inspeção e avaliação da conformidade do produto são feitas por etapas com a utilização de duas câmeras, uma captura a imagem do código de identificação alfanumérico do produto e a outra inspeciona o conjunto de elementos de fixação. As imagens passam por um processo de tratamento que envolve a filtragem espacial utilizando máscara de médias para suavização, alargamento de contraste para expandir a faixa de intensidades e segmentação para formação dos objetos de interesse. Uma função de OCR é utilizada para a extração de caracteres e reconhecimento do código do produto e a extração de características específicas do conjunto de componentes de fixação é feita por descritores de forma representados pelos invariantes de momento. As caracte... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Machine vision is a growing multidisciplinary field in the industry that is increasingly concerned with reducing costs, automating processes, and meeting product quality requirements to serve its customers. Manual assembly processes with inspection and visual control are typically error-prone processes using non-conforming parts in the final product assembly. This work presents a proposal for the development of a machine vision system based on digital image processing and analysis for the inspection of the characteristics and specifications of the parts and components used in the assembly of marine bonnets, aiming to verify and ensure the conformity of the final product. Inspection and conformity assessment of the product are done in stages using two cameras, one capturing the image of the alphanumeric identification code of the product and the other inspecting the set of fasteners. The images undergo a treatment process that involves spatial filtering using averaging masks for smoothing, contrast widening to expand the range of intensities, and segmentation to form the objects of interest. An OCR function is used for character extraction and product code recognition, and the extraction of specific features of the fastener assembly is done by shape descriptors represented by the moment invariants. The specific characteristics of the fasteners are used to assess the conformity of the product with its respective code. The presentation of data and results of the implemented prop... (Complete abstract click electronic access below)
Mestre
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Li, Chao. "WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/133.
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Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms.
A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern is captured which contains all the information about the penetration status. An experienced welder is able to determine weld penetration status just based on the reflected laser pattern. However, it is difficult to characterize the images to extract key information that used to determine penetration status. To overcome the challenges in finding right features and accurately processing images to extract key features using conventional machine vision algorithms, we propose using convolutional neural network (CNN) to automatically extract key features and determine penetration status.
Data-label pairs are needed to train a CNN. Therefore, an image acquiring system is designed to collect reflected laser pattern and the image of work-piece backside. Data augmentation is performed to enlarge the training data size, which resulting in 270,000 training data, 45,000 validation data and 45,000 test data. A six-layer convolutional neural network (CNN) has been designed and trained using a revised mini-batch gradient descent optimizer. Final test accuracy is 90.7% and using a voting mechanism based on three consequent images further improve the prediction accuracy.
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Malmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.
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Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. The findings also demonstrate examples of ensemble classifiers being effectively consolidated into unified models using the distillation technique. An analogue arrangement with optimization against multiple output targets, incorporating additional information, showed accuracy gains comparable to ensembling. For use of the classifier on test data with statistics different than those of the dataset, results indicate that augmentation of the input data during classifier creation helps performance, but would, in the current case, likely need to be guided by information about the distribution shift to have sufficiently positive impact to enable a practical application. I suggest, for future development, updated architectures, automated hyperparameter search and leveraging the bountiful unlabeled data potentially available from production lines.
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Palazzo, Simone. "Hybrid human-machine vision systems for automated object segmentation and categorization." Doctoral thesis, Università di Catania, 2017. http://hdl.handle.net/10761/3985.
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Emulating human perception is a foundational component in the research towards artificial intelligence (AI). Computer vision, in particular, is now one of the most active and fastest growing research topics in AI, and its field of practical applications range from video-survaillance to robotics to ecological monitoring. However, in spite of all the recent progress, humans still greatly outperform machines in most visual tasks, and even competitive artificial models require thousands of examples to learn concepts that children learn easily. Hence, given the objective difficulty in emulating the human visual system, the question that we intended to investigate in this thesis is in which ways humans can support the advancement of computer vision techniques. More precisely, we investigated how the synergy between human vision expertise and automated methods can be shifted from a top-down paradigm where direct user action or human perception principles explicitly guide the software component to a bottom-up paradigm, where instead of trying to copy the way our mind works, we exploit the by-product (i.e. some kind of measured feedback) of its workings to extract information on how visual tasks are performed. Starting from a purely top-down approach, where a fully-automated video object segmentation algorithm is extended to encode and include principles of human perceptual organization, we moved to interactive methods, where the same task is performed involving
humans in the loop by means of gamification and eye-gaze analysis strategies, in a progressively increasing bottom-up fashion. Lastly, we pushed this trend to the limit by investigating brain-driven image classification approaches, where brain signals were used to extract compact representation of image contents. Performance evaluation of the tested approaches shows that involving people in automated vision methods can enhance their accuracy. Our experiments, carried out at different degrees of awareness and control of the generated human feedback, show that top-down approaches may achieve a better accuracy than bottom-up ones, at the cost of higher user interaction time and effort. As for our most ambitious objective, the purely bottom-up image classification system from brain pattern analysis, we were able to outperform the current state of the art with a method trained to extract brain-inspired visual content descriptors, thus removing the need of undergoing EEG recording for unseen images.
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Bayraktar, Hakan. "Development Of A Stereo Vision System For An Industrial Robot." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605732/index.pdf.
Full textAbstract:
The aim of this thesis is to develop a stereo vision system to locate and
classify objects moving on a conveyor belt. The vision system determines the
locations of the objects with respect to a world coordinate system and class of the
objects. In order to estimate the locations of the objects, two cameras placed at
different locations are used. Image processing algorithms are employed to extract
some features of the objects. These features are fed to stereo matching and
classifier algorithms. The results of stereo matching algorithm are combined with
the calibration parameters of the cameras to determine the object locations.
Pattern classification techniques (Bayes and Nearest Neighbor classifiers) are
used to classify the objects. The linear velocity of the objects is determined by
using an encoder mounted to the shaft of the motor driving the conveyor belt. A
robot can plan a sequence of motion to pick the object from the conveyor belt by
using the output of the proposed system.
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Smith, Robert John. "Real-time surface flaw detection for the leather and textile industries using machine vision techniques." Thesis, University of Surrey, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308520.
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Belvedere, Danilo. "Analisi, Sviluppo e Sperimentazione di Sistemi di Visual Inspection in Contesti Industriali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22530/.
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Questa tesi è stata redatta durante il tirocinio svolto presso IBM Italia S.p.A (Bologna) e in collaborazione con l’azienda Automobili Lamborghini S.p.A.
In questa tesi si approfondiscono le tematiche legate al tema della Visual Inspection, che racchiude un insieme di metodi di computer vision impiegati per il controllo qualità di prodotti e/o step di processi in ambiente industriale attraverso verifiche visive.
Grazie alle più recenti tecniche di Machine Learning nell’ambito dei sistemi di Computer Vision oggi è possibile sviluppare sistemi in grado di apprendere costantemente il concetto di “difetto” apportando sostanziali miglioramenti in una miriade di ambienti e al contempo ampliando la platea degli utilizzatori industriali di queste tecniche. Avere la possibilità di delegare ad una macchina il cruciale compito delle ispezioni visive non solo riduce in maniera netta i costi del settore di controllo qualità ma garantisce all’azienda anche il riconoscimento di qualità ed eccellenza da parte dei clienti. In questa tesi verranno analizzati i principali software di Visual Inspection presenti nell’industria: si valuteranno le performance di piccole aziende specializzate in sistemi di visual inspection e sistemi proposti da grandi aziende come Microsoft, Amazon, Google ed IBM per poi spostarsi su alcuni casi di studio reale sviluppati sia in ambiente Open Source che con gli strumenti messi a disposizione da IBM Italia durante questo tirocinio. Nell’ultimo capitolo della tesi verrà trattato un caso di studio reale affrontato in collaborazione con l’azienda Lamborghini S.P.A con cui ho avuto modo di sperimentare le tecniche apprese durante questa esperienza in un caso di studio industriale.
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Thibault, Jean-Philippe. "Interprétation d'environnement évolutif par une machine de perception multi-sensorielle." Toulouse 3, 1993. http://www.theses.fr/1993TOU30038.
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Une machine de perception multi-sensorielle est un systeme informatique qui recoit continuement et va rechercher de facon active des informations sur son environnement, en vue de maintenir des interpretations coherentes de l'evolution de celui-ci. Ce travail porte sur une machine de perception dans un environnement en grande partie structure, avec beaucoup de connaissances sur ses caracteristiques, et une modelisation fine des objets y figurant. Les interpretations plausibles de l'environnement y sont limitees et peuvent etre specifiees sur la base de la modelisation effectuee. Les interpretations, supportees par les informations provenant des capteurs sensoriels, reposent sur la fusion des donnees, tant au niveau des informations numeriques issues des capteurs qu'au niveau des hypotheses d'interpretations partielles. La perception est active en ce sens que la machine de perception dispose de moyens de deplacer et de regler les modalites de fonctionnement des capteurs pour recueillir les informations qui lui font defaut. Notre contribution porte sur une gestion des hypotheses sensorielles, un raisonnement hypothetique a partir de ces hypotheses pour constituer des interpretations, un controle d'execution pour la fusion des hypotheses, un controle des interpretations emises et une gestion des moyens mis en oeuvre pour les obtenir. La premiere partie donne un etat de l'art des systemes de maintien de coherence et presente l'un d'eux, developpe pour le projet skids d'une machine de perception multi-sensorielle. La seconde partie traite de l'interpretation d'environnement dans notre machine de perception. Elle decrit l'obtention d'interpretations fiables a partir des donnees sensorielles percues. La troisieme partie presente les systemes de controle utilises dans la partie precedente. Le controle des interpretations resulte de la compilation des specifications de taches de perception
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Paccot, Flavien. "Contribution à la commande dynamique référencée capteurs de robots parallèles." Phd thesis, Clermont-Ferrand 2, 2009. http://www.theses.fr/2009CLF21929.
Full textAbstract:
Ces travaux de recherche concernent la prédiction et l'amélioration de la précision des machines à structure parallèle. Ces travaux s'intéressent principalement aux machines utilisées pour des tâches complexes comme la manipulation rapide d'objets ou l'usinage à grande vitesse. Ces travaux s'intéressent à la cohérence du triptyque modélisation - identification - commande. L'originalité de ces travaux est la prise en compte au niveau de la commande, et au moyen d'une perception adaptée, des spécificités de la machine et de la dynamique de la tâche. L'apport principal de ces travaux de thèse est de proposer une commande dynamique dans l'espace Cartésien tout en utilisant une mesure extéroceptive de la pose de l'effecteur. Cette stratégie de commande permet notamment une précision plus importante que les stratégies classiques. En effet, le nombre d'estimations utilisées est plus faible, principalement au niveau de l'estimation du comportement dynamique. De plus, la régulation dans l'espace Cartésien permet la maîtrise complète du mouvement de l'effecteur, notamment au niveau des singularités de la machine. Ces améliorations sont validées en simulation. Le développement d'un capteur visuel rapide à 1 kHz a également permis une validation expérimentale de ces travaux
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Paccot, Flavien. "Contribution à la commande dynamique référencée capteurs de robots parallèles." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2009. http://tel.archives-ouvertes.fr/tel-00725568.
Full textAbstract:
Ces travaux de recherche concernent la prédiction et l'amélioration de la précision des machines à structure parallèle. Ces travaux s'intéressent principalement aux machines utilisées pour des tâches complexes comme la manipulation rapide d'objets ou l'usinage à grande vitesse. Ces travaux s'intéressent à la cohérence du triptyque modélisation - identification - commande. L'originalité de ces travaux est la prise en compte au niveau de la commande, et au moyen d'une perception adaptée, des spécificités de la machine et de la dynamique de la tâche. L'apport principal de ces travaux de thèse est de proposer une commande dynamique dans l'espace Cartésien tout en utilisant une mesure extéroceptive de la pose de l'effecteur. Cette stratégie de commande permet notamment une précision plus importante que les stratégies classiques. En effet, le nombre d'estimations utilisées est plus faible, principalement au niveau de l'estimation du comportement dynamique. De plus, la régulation dans l'espace Cartésien permet la maîtrise complète du mouvement de l'effecteur, notamment au niveau des singularités de la machine. Ces améliorations sont validées en simulation. Le développement d'un capteur visuel rapide à 1 kHz a également permis une validation expérimentale de ces travaux.
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SORIANO, PINTER JAUME. "Machine learning-based image processing for human-robot collaboration." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278899.
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Human-robot Collaboration as a new paradigm in manufacturing has already been a hot topic in both manufacturing science, production research, intelligent robotics, and computer science. Due to the boost of deep learning technologies in the nearly ten years, advanced information processing technologies bring the new possibility to human-robot Collaboration. Meanwhile, machine learning-based image processing such as convolutional neural network has become a powerful tool in dealing with problems like target recognizing and locating. This kind of technologies shows potentials on robotic manufacturing and human-robot Collaboration. A challenge is to implement well-designed deep neural networks linked to a robotic system that can conduct collaborative works with the human. Accuracy and robustness need also be concerned in the development. This thesis work will address this challenge. This thesis tries to implement a solution based in Machine Learning methods for image detection which permits us to, using a low cost image solutions (RGB single camera), detect and localize manufacturing components to pick them and finish an assembly, helping the human co-workers, using an industrial robot, simplifying also the IT tasks to run it.Människa-robot samarbete, som ett nytt paradigm inom tillverkningsindustrin, har redan blivit ett omtalat ämne inom tillverkningsvetenskapen, produktforskningen, intelligent robotik och datavetenskapen. På grund av det senaste decenniets ökning av "deep learning" teknologier kan avancerade information-processerings teknologier bringa nya möjligheter för människarobot samarbete. Under tiden har även maskininlärnings-baserad bildklassificering med "convolutional neural network" blivit ett kraftfullt verktyg för att hantera problem så som måligenkänning och lokalisering. Dessa typer av teknologier har potential att implementeras nom robotiserad tillverkning och människa-robot samarbete. En utmaning är att implementera väldesignade "convolutional neural networks" kopplat till ett robot system som kan utföra arbete i samarbete med människan. Noggranhet och robusthet behöver också avvägas i utvecklingsarbetet. Detta examensarbete kommer att ta itu med denna utmaning. Detta examensarbete försöker att implementera en lösning baserad på maskininlärnings-metoder för bildigenkänning som tillåter oss att, med hjälp av en billig bild lösning (RGB enkel kamera), detektera och lokalisera tillverkningskomponenter att plocka upp och slutföra en montering, vilket hjälper den mänskliga medhjälparen, med en industriell robot. Detta förenklar också IT-uppgifterna för att köra den.
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Mingoto, Junior Carlos Roberto. "Método de medição de alinhamento de suspensão veicular não intrusivo baseado em visão computacional." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264577.
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Orientador: Paulo Roberto Gardel KurkaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-21T02:23:47Z (GMT). No. of bitstreams: 1 MingotoJunior_CarlosRoberto_D.pdf: 4676498 bytes, checksum: f396cb633ba04f6ff1589cea747bb133 (MD5) Previous issue date: 2012
Resumo: O presente projeto de pesquisa aplica técnicas de visão estereoscópica computacional no desenvolvimento da configuração de um equipamento de medição de ângulos de alinhamento de suspensão veicular, usando câmeras de vídeo de baixo custo. Atualmente, a maioria dos dispositivos de medição de ângulos de alinhamento de suspensão de veículos baseia-se no uso de componentes eletromecânicos, como pêndulos resistivos, inclinômetros capacitivos, dispositivos opto-mecânicos (espelhos e raio de luz monocromática de baixa intensidade). Com a sequência aqui estabelecida dos fundamentos algébricos e técnicas de visão computacional, realizam-se estudos de viabilidade científica e proposta de construção de um equipamento de verificação de ângulos de alinhamento veicular. São apresentados testes virtuais e reais, ilustrativos da potencialidade operacional do equipamento
Abstract: This research project uses stereoscopic computer vision techniques to develop a system to measure alignment angles of vehicular suspensions, using low cost cameras. Currently, most of the devices intended to measure vehicular suspension angles are based on the use of electromechanical components, such as resistive pendulums, capacitive inclinometers or opticmechanical devices (mirrors and projection of beams of monochromatic light of low intensity). Fundaments of linear algebra and computer vision techniques, lead to studies of feasibility and practical implementation of a system used to measure vehicular suspension alignment angles. Virtual and real measurements are carried out to illustrate the operative potential of such a system
Doutorado
Mecanica dos Sólidos e Projeto Mecanico
Doutor em Engenharia Mecânica
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Banús, Paradell Núria. "New solutions to control robotic environments: quality control in food packaging." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/673469.
Full textAbstract:
Machine vision systems and artificial intelligence techniques are two active research areas
in the context of Industry 4.0. Their combination allows the reproduction of human
procedures while improving the performance of the processes. However, to achieve the
desired full automation, there is a need for new applications able to cover as many
industrial scenarios and processes as possible. One of the areas that needs further research
and development is the quality control of food packaging, and more specifically
in the closure and sealing control of thermoforming packages. The shortcomings in this
area were detected by TAVIL who, in collaboration with GILAB, proposed an Industrial
Doctorate to investigate, develop and integrate in real scenarios new methods to
improve the packaging stage of the food industry by using machine vision systems and
artificial intelligence techniques. In the context of this Industrial Doctorate, two focuses
of research were defined that differ at the level at which the problem is studied. The
first focused on the quality control of food packages, and the second on the efficient
management of machine vision systems in industrial scenariosEls sistemes de visió per computador i les tècniques d’intel·ligència artificial són dues àrees de recerca actives en el context de la Indústria 4.0. La seva combinació permet la reproducció de procediments humans millorant al mateix temps el rendiment dels processos. Malgrat això, per aconseguir l’automatització completa desitjada, hi ha la necessitat de noves aplicacions capaces de cobrir el màxim d’escenaris i processos industrials possibles. Una de les àrees que necessita més investigació i desenvolupament és el control de qualitat dels envasos d’aliments, i més concretament, el control del tancament i del segellat d’envasos termoformats. Les necessitats en aquesta àrea van ser identificades per TAVIL que, amb col·laboració amb GILAB, van proposar un Doctorat Industrial per investigar, desenvolupar i integrar en escenaris reals nous mètodes per millorar l’etapa d’envasat de la indústria alimentària mitjançant sistemes de visió per computador i tècniques d’intel·ligència artificial. En el context d’aquest Doctorat Industrial, s’han seguit dues línies d’investigació que es diferencien en el nivell en el qual estudien el problema. La primera línia es basa en el control de qualitat d’envasos d’aliments, mentre que la segona es basa en el control eficient de sistemes de visió per computador en escenaris industrials
Programa de Doctorat en Tecnologia
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Joseph, Anand Emmanuel, and Zafra Luis Carlos Chica. "Evaluation of a medium-sized enterprise’s performance by data analysis : Introducing innovative smart manufacturing perspectives." Thesis, KTH, Industriell produktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261351.
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Small and medium-sized enterprises are highly limited on resources for the transformation into smart factories. Nytt AB, a new startup specialized in smart manufacturing solutions, is completely focused on taking down the barriers with a basic solution: implementing a machine vision system with the purpose to monitor the machines of the factories. The main aim of this thesis is to analyze the data collected from two different machines of a medium-sized factory by monitoring the color states of the stack lights.First of all, some topics are analyzed in order to get a better understanding and knowledge of the main topic of this thesis: smart manufacturing. Secondly, the methodology used during the project is explained. Thirdly, the product developed by Nytt AB is described to get a better understanding. Together with this, the companies where the product is implemented are described. The next step is the presentation of the results by analyzing the data according to these parameters:(i), the availability of the machines, (ii), critical machine tool analysis; (iii),machine idling time; (iv), disruption events; and finally, (v), information transfer. In the results, some graphs and discussions are presented. In the following chapter the conclusions are presented, which allow the analyzed company to improve its current state. Lastly, the relocation of the product into the critical machine, the implementation of new sensors to detect temperature and vibration values of the machines and the implementation of the module OpApp within the factories are suggestions presented as future work at the end of this report.Små och medelstora företag har mycket begränsade resurser för omvandling till smarta fabriker. Nytt AB, ett nystartat företag inom smart tillverkning, är helt fokuserad på att ta bort hinder med en enkel lösning: implementering av ett kamerasystem för övervakning av maskiner i fabriker. Huvudsyftet med detta examensarbete är att analysera data som samlats in från två olika maskiner i en medelstor fabrik genom att övervaka färgändringar i deras ljuspelare. För det första analyseras några ämnesområden för att få en bättre förståelse och kunskap om huvudtemat i detta examensarbete: smart tillverkning. För det andra förklaras den metod som används under projektet. För det tredje beskrivs den produkt som utvecklats av Nytt AB för att få en bättre förståelse. Tillsammans med detta beskrivs de företag där produkten implementeras. Nästa steg är presentationen av resultatet genom att analysera data enligt följande parametrar:(i), maskinens tillgänglighet; (ii), kritisk verktygsmaskinanalys; (iii), maskinens tomgångstid; (iv), störningshändelser och slutligen; (v), informationsöverföring. I resultatet presenteras några grafer och diskussioner. Slutsatserna presenteras därefter. Dessa slutsatser gör att det analyserade företaget kan förbättra sitt nuvarande tillstånd. Som framtida arbete föreslås slutligen flytt av kamerasystemet till den kritiska maskinen, införande av nya sensorer för att övervaka temperaturer och vibrationsvärden för maskinerna och implementeringav modulen OpApp i fabriker.
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FITTI, Matteo. "Non-contact smart measurement systems for in-line quality control of precision turned parts." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274500.
Full textAbstract:
Questa tesi è il risultato del lavoro svolto nell'ambito del progetto europeo H2020 GO0D MAN e intende essere un contributo alla realizzazione del paradigma dell' Industry 4.0. In particolare, riguarda lo sviluppo di sistemi intelligenti di controllo qualità in linea.
Il lavoro presenta lo sviluppo di due sistemi: a) un sistema di misura automatizzato basato su un sensore cromatico confocale per controllare le dimensioni dei componenti metallici torniti e b) un sistema di controllo automatizzato basato su visione artificiale per verificare la presenza di bave dovute al processo di foratura sulle stesse parti.
Questi componenti metallici, costituiti da piccoli cilindri cavi con numerosi fori laterali, sono utilizzati nelle valvole idrauliche destinate all'industria automobilistica. Il controllo di qualità di queste parti richiede la verifica di tolleranze dimensionali rigorose e delle prestazioni funzionali.
L'obiettivo generale del progetto è realizzare un controllo di qualità in linea al 100% al fine di prevenire la generazione e la propagazione di difetti all'uscita della stazione di lavorazione di tornitura.
I due sistemi sviluppati presentano un comportamento intelligente, finalizzato alla gestione dell'incertezza di misura e alla riduzione del rischio di diagnosi errate. Il livello di automazione e l'approccio ottico senza contatto adottato consentono di estendere i controlli precedentemente effettuati su campioni statistici al 100% della produzione.
Dopo aver presentato i casi industriali, questa tesi discute la progettazione concettuale di ciascun sistema, i passaggi per la realizzazione dei prototipi, la loro caratterizzazione in condizioni di laboratorio e infine la dimostrazione in una vera linea di produzione.This thesis is the result of work carried out within the European project H2020 GO0D MAN and intends to be a contribution to realize the Industry 4.0 paradigm. In particular, it concerns the development of in-line smart quality control systems. The work presents the development of two systems: a) an automated measurement system based on a confocal chromatic sensor for checking the dimensions of turned metal components and b) an automated control system based on computer vision for checking the presence of burrs due to the drilling process on the same parts. These metal components, which consist of little hollow cylinders with several lateral holes, are used in hydraulic valves intended for use in the automotive industry. The quality control of these parts requires the verification of stringent dimensional tolerances and functional performances. The overall objective of the project is to realize an in-line 100% quality control in order to prevent the generation and propagation of defects at the exit of the turning processing station. The two systems developed exhibit smart behaviour, aimed at managing measurement uncertainty and reducing the risk of misdiagnosis. The automation level and the optical non-contact approach adopted allows to extend the controls previously made on statistical samples to 100% of the production. After presenting the industrial cases, this thesis discusses the conceptual design for each system, the steps for the realization of the prototypes, their characterization in laboratory condition and finally the demonstration in a real production line.
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ALTIERI, ALEX. "Yacht experience, ricerca e sviluppo di soluzioni basate su intelligenza artificiale per il comfort e la sicurezza in alto mare." Doctoral thesis, Università Politecnica delle Marche, 2021. http://hdl.handle.net/11566/287605.
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La tesi descrive i risultati dell’attività di ricerca e sviluppo di nuove tecnologie basate su tecniche di intelligenza artificiale, capaci di raggiungere un’interazione empatica e una connessione emotiva tra l’uomo e “le macchine” così da migliorare il comfort e la sicurezza a bordo di uno yacht. Tale interazione è ottenuta grazie al riconoscimento di emozioni e comportamenti e alla successiva attivazione di tutti quegli apparati multimediali presenti nell’ambiente a bordo, che si adattano al mood del soggetto all’interno della stanza. Il sistema prototipale sviluppato durante i tre anni di dottorato è oggi in grado di gestire i contenuti multimediali (ad es. brani musicali, video riprodotti nei LED screen) e scenari di luce, basati sull'emozione dell'utente, riconosciute dalle espressioni facciali riprese da una qualsiasi fotocamera installata all’interno dello spazio. Per poter rendere l’interazione adattativa, il sistema sviluppato implementa algoritmi di Deep Learning per riconoscere l’identità degli utenti a bordo (riconoscimento facciale), il grado di attenzione del comandante (Gaze Detection e Drowsiness) e gli oggetti con cui egli interagisce (telefono, auricolari, ecc.). Tali informazioni vengono processate all’interno del sistema per identificare eventuali situazioni di rischio per la sicurezza delle persone presenti a bordo e per controllare l’intero ambiente. L’applicazione di queste tecnologie, in questo settore sempre aperto all’introduzione delle ultime innovazioni a bordo, apre a diverse sfide di ricerca.The thesis describes the results of the research and development of new technologies based on artificial intelligence techniques, able to achieve an empathic interaction and an emotional connection between man and "the machines" in order to improve comfort and safety on board of yachts. This interaction is achieved through the recognition of emotions and behaviors and the following activation of all those multimedia devices available in the environment on board, which are adapted to the mood of the subject inside the room. The prototype system developed during the three years of PhD is now able to manage multimedia content (e.g. music tracks, videos played on LED screens) and light scenarios, based on the user's emotion, recognized by facial expressions taken from any camera installed inside the space. In order to make the interaction adaptive, the developed system implements Deep Learning algorithms to recognize the identity of the users on board (Facial Recognition), the degree of attention of the commander (Gaze Detection and Drowsiness) and the objects with which he interacts (phone, earphones, etc.). This information is processed within the system to identify any situations of risk to the safety of people on board and to monitor the entire environment. The application of these technologies, in this domain that is always open to the introduction of the latest innovations on board, opens up several research challenges.
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XIE, MIN-HAN, and 謝旻翰. "Implementation of Machine Vision Inspection Technique for Industrial Automatic Production." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r3x4n6.
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碩士修平科技大學
電機工程碩士班
107
As time goes by, the industries step into intelligent and automatic generation. In order to catch up the trend, we apply mechanical vision system to variety of significant industrial automation broadly. The mechanical vision system can help us detect the appearance of the products, defection of the package. Moreover, the circuit board was divided into several areas, and distinguish the components whether they are welded accurately or not. Through the system, we can effectively lower the error rate of the function, and enhance the quality of products. The edge detection method based on searching is usually expressed by first derivative, such as Prewitt, Roberts, and so on. Furthermore, zero-crossing method is useful for us to find out the zero-crossing points, then position edges. According to characteristics of detecting methods, we apply suitable method to corresponding images, and carry out edge detection. FH, the Omron image processing system, is the main software in the study. It’s a original strength and innovative inspection technique. Matching with different lens and light source regulators, and it can be applied more widely. The application cases are as follow: identifying the inner and outer labels of the bottle caps, distinguishing size of the relay riveting spring and finding out stain on the surface of instant noodles. To find out the best lens and brightness in the testing process, then the original figure was present at its best, which was deal with various types of image processing, such as expansion, color filtering, background elimination, pre-measurement processing, shape searching, etc. After setting image processing procedures, we can use image statically identification to find out the defective product.
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Soares, João Cachulo. "Valve and Steam Trap Component Recognition Using Machine Vision in an Industrial Application." Master's thesis, 2016. http://hdl.handle.net/10316/81637.
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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e TecnologiaNo presente trabalho é apresentada uma solução para um sistema autónomo de identificação/reconhecimento capaz de classificar componentes de válvulas e purgadores, numa aplicação industrial de pintura, usando reconhecimento supervisionado de padrões baseado em visão. O sistema de visão aqui proposto tem por objectivo servir de base para uma solução a ser instalada numa unidade fabril de uma empresa especializada no fabrico de equipamentos para vapor, por forma a complementar a modernização e e automação do processo. Este processo passaria a contar como robôs para proceder à pintura dos produtos ao invés de pessoas, utilizando programas específicos chamados de acordo com o resultado do processo de identificação do produto, realizado à priori.Começou-se por criar um conjunto de dados que incluiu o grupo dos produtos mais produzidos/vendidos pela empresa, recolhendo imagens num setup semelhante àquele que poderíamos montar no ambiente industrial. O passo seguinte consistiu no pré-processamento das imagens extraídas. De seguida são aplicadas técnicas de processamento de imagem para o tratamento e binarização das imagens. Nesta etapa é ainda desenvolvido um algoritmo para a remoção das pinças que penduram as peças em posição para pintura. Neste momento estamos na presença de imagens binárias com \textit{blobs} que representa exclusivamente os produtos. O passo seguinte consistiu na implementação de dois métodos de extração de características das imagens. O primeiro método é baseado na extração características da forma dos \textit{blobs}, seguido de uma implementação de um descriptor HOG. Ambas as técnicas são posteriormente usadas nas imagens resultantes do pré-processamento, sendo que as características extraídas são utilizada para treinar um classificador discriminativo e generativo, respetivamente um SVM (máquina de vectores de suporte) para classificação de múltiplas classes e um NBC (classificador bayesiano ingênuo). No que diz respeito aos resultados de classificação, o SVM provou ser a melhor solução em termos de desempenho, velocidade e robustez quando comparado com o NBC. Relativamente à escolha entre as features geométricas baseadas em formas e as features extraídas ao utilizar o descritor HOG, concluiu-se que as primeiras mostraram melhor resultados no que diz respeito ao reconhecimento de maior número de imagens, mostrando precisões de $100\%$ para toda a gama de \textit{thresholds}. Os resultados para a revocação foram igualmente elevados, neste caso para \textit{thresholds} abaixo dos $0.65-0.70$.
In this work an autonomous identification/recognition system capable of classifying valve and steam trap components in an industrial painting application was implemented, using vision-based supervised pattern recognition. The proposed vision system has the main objective of being a foundation for a solution to be installed in the manufacturing facilities of a company specialized in steam equipment, in order to complement the modernization and automation of the process. The process would rely on robots instead of human beings, using specific programs which would be called depending on a prior product identification result. The first step corresponds to the creation of a dataset with a group of the best-selling/most produced products, grabbing frames from a image acquisition scenario similar to the one possibly built in the industrial environment. The following step consists in pre-processing, where image processing techniques are introduced to threshold and treat the images as well as removing the claw that holds the products in position for painting. At this point the image contains a blob that exclusively represents the products. The following step consists in the implementation of two feature extraction methods. Firstly blob features based on shape and overall geometric characteristics, followed by a HOG implementation. Both feature extraction techniques are then used on the post-processing images and are trained on a discriminative and generative classifier, respectively a multiclass Support Vector Machine and Naive Bayes classifier. In terms of classification results, the SVM proved to be the best solution in terms of performance, speed, and robustness, outclassing the NBC. Regarding the choice between blob features or HOG features, it was concluded that the blob features would do a better job in describing the objects, showing results with $100\%$ precision for all possible threshold values, and recalls equally high for thresholds below $0.65-0.70$
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Lin, Cheng-Wei Tsai, and 蔡林承緯. "Integration of the Machine Vision and the Robotic Manipulators for the Industrial Application." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/pat87g.
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碩士國立虎尾科技大學
自動化工程系碩士班
105
Customized manufacturing is increasing years by years, how the manufactory imports the automatic equipment to the production line is the important topic. The measurement time and the error of inspection is decreased when the machine vision replace the human source. Besides, the study completed the system of automatic loading and unloading for automatic production by manipulator and machine vision. First, the study derived and proved the Kinematics of manipulator, but the study used the commercially available manipulator to experiment. The study of vision inspection is divided into the target of static seek and the dynamic target tracking object to experiment. For the target of static seek, the picture of machine vision to image of processing, and corrected the coordinate of machine vision and manipulator to the same origin by the material’s position from the picture of the machine vision. After the coordinate synchronizing, the machine vision and manipulator completed the function the material of gripping by serial transmission. The experimental of dynamic target tracking use the concept of the target of static seek to complete the coordinate synchronize between the manipulator and the machine vision. Then, the velocity of the conveyor will be determined by time interlude and moving distance from machine vision in the program by using Labview software. At the same time, the program used the velocity of the conveyor and the position of material from machine vision to predict the position of target. The manipulator is loading and unloading the material when the program is issue the command. Finally, the study completed the function of automatic loading and unloading by serial transmission and I/O signal for the equipment.
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Tabish, Muhammad. "Machine Vision Control Algorithm Design for Industrial Palletizing Robo Machines to Increase the Dynamic Stability by Real-Time Image Processing." Thesis, 2020. https://vuir.vu.edu.au/41808/.
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The focus of the research is to palletise the laser cut irregular objects of metal, wood and marble. The large and heavy regular objects are very difficult to palletise by humans, even in the presence of manual palletisers. This becomes more complicated when the objects are of irregular shape. These objects are cut by precise laser into any shape, size and weight. Due to irregularity on the boundary and perforation inside the boundary makes it complicated for the Robots to palletise. Since palletising Robots are designed to grasp the objects from the fix spots and are preferred to be used for repeating jobs of same size and shape from same position. Therefore, Robot handling is also prohibited due to vast geometrical variation in objects.
This issue has been raised in manufacturing industries that uses CNC (Computer Numeric Control) machines to mill or laser cut of large sheets. These sheets are commercialised in variety of most known materials like wood, marble and steel. Initially, all sheets are of regular shape mostly a rectangle with standard size of 1220mm x 2440mm observed in the wood industry. Since this configuration favours the Robot to palletise from pre-defined spot to the machine bed and cuts off the material into different shapes using precise laser. Once the laser cutting process is completed, the shape and size of the sheets are unpredicted, and this configuration is beyond Robot limitations therefore human handling is required. To develop a fully automated system and avoid heavy manual lifting, the Robot is necessary to collaborate with the environment by real time feedback system and integrate a controller to understand and solve the complex irregularity problems. This way the Robot can be used for non-repetitive task at unknown predefined spots.
The Robot currently working on commercial scale uses the pneumatic grippers to palletise regular sheets. Some Robots have the capability to deal with irregular objects with limitations. These Robots pick the objects from COM (Centre of Mass) since they are very small in size and does not have sharp edges or perforation. The COM is a good technique for palletising only, if the objects are not too heavy or does not have much irregularities on the boundary. When a sheet is cut in a star shape with a hole at the centre or a grill type perforated having only 30 % of material after laser cutting, these scenarios are not yet been researched.
The research proposes a MV (Machine Vision) controller that is designed, simulated on MATLAB (Matrix Laboratory) software and validated by implementing on a Robot in real time. The Algorithm is developed to work in a loop that repeat its cycle until or unless a human intervention subroutine is requested. The software takes images of irregular objects after fixed interval of time and evaluate the features of the shape. The image is disintegrated into finite small regular polygons through real-time image processing to formulate the trajectory. This trajectory is further analysed to configure the spot where the object can be grasped. Once the calculation is completed the MATLAB Algorithm communicate with the Robot controller and shares the positional information to the Robot. Now the Robot controller check the possibility to reach all the position and postures of manipulator. Further, this information is sent to check the range of end effector and enable it to start the operation. The whole system is in a feedback loop, if the object is dropped between operation due to miscalculation or mishandling. The Robot will stop and ask the MATLAB to re-evaluate the position of dropped object. If MATLAB is unable to calculate the trajectory for this object, the whole system will shut down and wait for human assistance.
The robustness of the proposed method is evaluated through MATLAB simulations. To appreciate the validation of the Algorithm it is necessary to develop a prototype. Therefore, a 5-Axis Serial Robot (Mitsubishi RM-501) is used and the controller of this Robot is developed to read the information from MV system and integrate the pneumatic end effector with the Robot. F280049C Launchpad is used as main control unit of the Robot to control actuator, sensors and to communicate with MATLAB. MACH3 is also used as Robot interface software more details are available in chapter 5.
The research has also been integrated in a project at R&D Department of a medical device manufacturer in Australia. The research internship provided development of an AUWS to weld soft polymeric materials together. The main objective after developing the machine from scratch was to weld these medical devices of different size and shapes. Therefore, MV technique is used to generate the different regular and irregular bonding pattern that could results in strong weld joints. Furthermore, the position and torque of ultrasonic welding head were also controlled based on thickness. The project is working and producing the medical devices for research purpose.
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Ματθαιάκης, Αλέξανδρος-Στέργιος. "Εφαρμογές αυτοματοποίησης ρομποτικών διαδικασιών." Thesis, 2012. http://hdl.handle.net/10889/6391.
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Η σύγχρονη τάση επιβάλει στα ρομπότ να μπορούν πιο εύκολα να προσαρμοστούν στο περιβάλλον. Οι λόγοι που επιβάλλουν κάτι τέτοιο είναι κυρίως λόγοι οικονομίας χρήματος και χρόνου. Για να είναι δυνατόν να μπορεί να προσαρμοστεί το βέλτιστο τρόπο θα πρέπει να μπορεί να λαμβάνει σαν είσοδο πληροφορία από αυτό. Διάφορα είδη αισθητηρίων χρησιμοποιούνται για αυτό το σκοπό.
Για τη παρούσα διπλωματική εργασία αναπτύχθηκε ένα σύστημα στερεοσκοπικής όρασης. Η λογική για ένα τέτοιο σύστημα είναι να λειτουργεί σαν τα μάτια του ρομπότ, δηλαδή να εντοπίζει με αυτοματοποιημένο τρόπο τα σημεία στα οποία θα πρέπει να μεταβεί το ρομπότ για να ολοκληρώσει την εκάστοτε εργασία (πιάσιμο αντικειμένου, συγκόλληση κλπ).
Πειράματα αναπτύχθηκαν γύρω από το αντικείμενο αυτό, με σκοπό τη συλλογή μετρήσεων διαφόρων σημείων με χρήση κάμερας (3D), προκειμένου να οδηγηθεί το ρομπότ σε αυτά μέσω των σημείων που αναγνώριζε ο αλγόριθμος επεξεργασίας εικόνας. Η υποστήριξη των πειραμάτων αυτών έγινε από το σχεδιασμό και τον προγραμματισμό ενός συστήματος στερεοσκοπικής όρασης για την οδήγηση του ρομπότ στην ολοκλήρωση συγκολλήσεων των εκάστοτε σημείων.The current trend in robotics concerns the easy adaption in the environment. The reasons for requiring this are mainly economic reasons and time effective processes. In order to help the robot to be adjusted optimally, the second should be able to take as input information from it. Various kinds of sensors are used for this purpose. A stereo vision system was developed in this thesis. The rationale for such a system is to act as the eyes of the robot, e.g. to identify an automated way in which the robot should make motion planning in order to completer to each task (grasping the object, welding etc.). Experiments were developed around the object, in order to collect measurements using different camera points (3D), in order to guide the robot through these points that recognized the image processing algorithm. The supporting of these experiments were the design and planning of a stereo vision system for driving the welding robot in completing the respective points
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"Bediener-Assistenzsysteme für Verarbeitungsmaschinen – Konzepte & Visionen: VVD-Anwenderforum 2017 am 26.09.2017 in Dresden." Fraunhofer-Institut für Verfahrenstechnik und Verpackung IVV, 2017. https://slub.qucosa.de/id/qucosa%3A16476.
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