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Dissertations / Theses on the topic 'Machine-tools – Monitoring'
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Van, den Berg Gideon. "Hidden Markov models for tool wear monitoring in turning operations." Diss., Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05302005-114238/.
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Hoh, See Min. "Condition monitoring and fault diagnosis for CNC machine tools." Thesis, Cardiff University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295120.
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Harris, C. G. "Fault diagnosis and condition monitoring for NC/CNC machine tools." Thesis, Cardiff University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381227.
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Marzi, M. Hosein. "An intelligent condition monitoring system with applications to machine tools." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265591.
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Sztendel, Sebastian. "Model referenced condition monitoring of high performance CNC machine tools." Thesis, University of Huddersfield, 2016. http://eprints.hud.ac.uk/id/eprint/34112/.
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Generally, machine tool monitoring is the prediction of the system’s health based on signal acquisition and processing and classification in order to identify the causes of the problem. The producers of machine tools need to pay more attention to their products life cycle because their customers increasingly focus on machine tool reliability and costs. The present study is concerned with the development of a condition monitoring system for high speed Computer Numerical Control (CNC) milling machine tools. A model is a simplification of a real machine to visualize the dynamics of a mechatronic system. This thesis applies recent modelling techniques to represent all parameters which affect the accuracy of a component produced automatically. The control can achieve an accuracy approaching the tolerance restrictions imposed by the machine tool axis repeatability and its operating environment. The motion control system of the CNC machine tool is described and the elements, which compose the axis drives including both the electrical components and the mechanical ones, are analysed and modelled. SIMULINK models have been developed to represent the majority of the dynamic behaviour of the feed drives from the actual CNC machine tool. Various values for the position controller and the load torque have been applied to the motor to show their behaviour. Development of a mechatronic hybrid model for five-axis CNC machine tool using Multi-Body-System (MBS) simulation approach is described. Analysis of CNC machine tool performance under non-cutting conditions is developed. ServoTrace data have been used to validate the Multi-body simulation of tool-to-workpiece position. This thesis aspects the application of state of art sensing methods in the field of condition monitoring of electromechanical systems. The ballscrew-with-nut is perhaps the most prevalent CNC machine subsystem and the condition of each element is crucial to the success of a machining operation. It’s essential to know of the health status of ballscrew, bearings and nut. Acoustic emission analysis of machines has been carried out to determine the deterioration of the ballscrew. Standard practices such as use of a Laser Interferometer have been used to determine the position of the machine tool. A novel machine feed drive condition monitoring system using acoustic emission (AE) signals has been proposed. The AE monitoring techniques investigated can be categorised into traditional AE parameters of energy, event duration and peak amplitude. These events are selected and normalised to estimate remaining life of the machine. This method is shown to be successfully applied for the ballscrew subsystem of an industrial high-speed milling machine. Finally, the successful outcome of the project will contribute to machine tool industry making possible manufacturing of more accurate products with lower costs in shorter time.
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Repo, Jari. "Condition monitoring of machine tools and machining processes using internal sensor signals." Licentiate thesis, KTH, Machine and Process Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-12872.
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Condition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and achieving a more robust machining process. Failures in the machining process and machine tool components may also have negative effects on the final produced part. Instabilities in machining processes also shortens the life time of the cutting edges and machine tool.
The condition monitoring system may utilise information from several sources to facilitate the detection of instabilities in the machining process. To avoid additional complexity to the machining system the use of internal sensors is considered. The focus in this thesis has been to investigate if information related to the machining process can be extracted directly from the internal sensors of the machine tool.
The main contibutions of this work is a further understanding of the direct response from both linear and angular position encoders due the variations in the machining process. The analysis of the response from unbalance testing of turn tables and two types of milling processes, i.e. disc-milling and slot-milling, is presented. It is shown that operational frequencies, such as cutter frequency and tooth-passing frequency, can be extracted from both active and inactive machine axes, but the response from an active machine axis involves a more complex analysis. Various methods for the analysis of the responses in time domain, frequency domain and phase space are presented.
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Peng, Jie. "Design and analysis of a CNC system for machining and monitoring." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/29730.
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The development of unmanned machining systems has been a recent focus of manufacturing
research. Computer Numerical Control (CNC) units, which monitor and control many tightly coupled machining tasks, are inseparable parts of the unmanned manufacturing
system. This thesis addresses the detailed design and analysis of a modular CNC system for a milling machine. The feed drive control system of the machine tool is thoroughly studied. The mathematical model for the feed drive control system, which consists of a motion controller, power amplifiers and DC-servo motors, is developed and experimentally verified. A method of estimating cutting forces from current drawn by feed drive motors is developed. The viability of the current sensor as a feeding-force sensing component is illustrated and experimentally tested on the milling machine. Successful
application of the current sensor to tool breakage detection in milling operation is presented. The performance of the multi-axis contouring system in precision machining is discussed. The contouring errors induced by feed drive positioning systems in CNC machine tools is investigated using the state space model of the CNC system. The influence
of the cutting force on the accuracy of machining is shown to be negligible for feed drives driven by ball leadscrews. Various control strategies for the improvement of machining accuracy are tested in the simulation studies.Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Guo, Jianying, and 郭建英. "Verification of HKU-DPM results by pollout tests and drillhole logs inweathered tuff." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26663983.
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Onwuka, Goodness Raluchukwu. "Ultra-high precision grinding of BK7 glass." Thesis, Nelson Mandela Metropolitan University, 2016. http://hdl.handle.net/10948/5203.
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With the increase in the application of ultra-precision manufactured parts and the absence of much participation of researchers in ultra-high precision grinding of optical glasses which has a high rate of demand in the industries, it becomes imperative to garner a full understanding of the production of these precision optics using the above-listed technology. Single point inclined axes grinding configuration and Box-Behnken experimental design was developed and applied to the ultra-high precision grinding of BK7 glass. A high sampling acoustic emission monitoring system was implemented to monitor the process. The research tends to monitor the ultra-high precision grinding of BK7 glass using acoustic emission which has proven to be an effective sensing technique to monitor grinding processes. Response surface methodology was adopted to analyze the effect of the interaction between the machining parameters: feed, speed, depth of cut and the generated surface roughness. Furthermore, back propagation Artificial Neural Network was also implemented through careful feature extraction and selection process. The proposed models are aimed at creating a database guide to the ultra-high precision grinding of precision optics.
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Amer, Waseem. "Distributed embedded system with internet GSM connectivity for intelligent e-monitoring of machine tools." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56071/.
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Machining is one of the most important operations in many industrial environments. To prosper in today's competitive industrial world any machining system should be able to deliver the highest possible quality at the lowest possible costs, with very high reliability and flexibility. To fulfil these requirements the idea of e-Monitoring an industrial process was introduced by the Intelligent Process Monitoring and Management (IPMM) Centre at Cardiff University. It has considerable potential applications in industrial systems to not only monitor the health of the machines but also for data management and presentation for future decision making. The research presented in this thesis considers the evolution of two different low complexity signal analysis techniques which can be used for e-Monitoring the health of the cutters used in milling machine tools. The researched techniques are based in the time and frequency domains. The frequency domain analysis technique is based on the idea of using switched capacitor filters and microcontrollers to monitor the frequencies of interest in existing machine tool signals (spindle load and speed) thus avoiding the need for external sensors. The results of frequency domain analysis are used to assess the health of the cutter. The time domain analysis technique uses the same signals to analyse any variations within a tool rotation period and relate these to the health of the cutter. The results are integrated before final decision making which helps in reducing false alarms. The thesis goes on to logically describe the design and development of an on-line microcontroller based distributed intelligent e-Monitoring system for a milling machine tool model Kondia B500, using the proposed signal analysis techniques. Some additional features such as internet and GSM connectivity have also been added to the designed system. The designed system was interfaced to the machine tool and tested for its reliability which was found to be competitive with many other very expensive systems. The designed system can be fitted into a machine tool at the manufacturing stage or it could be interfaced to an existing machine tool for automatically detecting a tooth breakage.
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Quintana, i. Badosa Guillem. "Stability lobes diagram identification and surface roughness monitoring in milling processes." Doctoral thesis, Universitat de Girona, 2010. http://hdl.handle.net/10803/7769.
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La millora de la productivitat i la qualitat són indubtablement dues de les principals exigències del sector productiu modern i factors clau per la competitivitat i la supervivència. Dins aquest sector,la fabricació per arrancada de material juga encara avui en dia un paper protagonista tot i l'aparició de noves tècniques de conformat per addició.Indústries com l'aeronàutica, l'automobilística,la del motlle o l'energètica, depenen en bona part de les prestacions de les màquines-eina. Aquesta Tesi aborda dos aspectes rellevants quan es tracta de millorar de la productivitat i la qualitat del sector productiu: el problema del fimbrament, més conegut per la denominació anglosaxona chatter,i la monitorització de la rugositat superficial en el mecanitzat a alta velocitat.Productivity and quality improvement are undoubtedly two of the main demands of the
modern manufacturing sector and key factors for competitiveness and survival. Within this sector, material removal processes play, still nowadays, a principal role despite the emergence of additive manufacturing techniques. Industries such as aerospace, automotive, molds and dies or energy largely depend on machine tools performance for improved productivity and quality. This Thesis is focused on two important aspects when it comes to improving productivity and quality of the manufacturing sector: chatter problem, and surface roughness monitoring in high speed milling.
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Wilmot, Wessley. "Process and machine improvements and process condition monitoring for a deep-hole internal milling machine." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/process-and-machine-improvements-and-process-condition-monitoring-for-a-deephole-internal-milling-machine(2bb87f60-aa39-4fff-a82a-9360ce36b74c).html.
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Milling is a widely used cutting process, most commonly applied to machining external surfaces of workpieces. When machining operations are required within hard to reach areas of components, or deep within the bore of components, alternative methods of metal removal are generally employed. Typically when milling at extended reaches, difficulties may increase exponentially when trying to achieve distances several meters into a component. Essentially every topic of the milling process becomes difficult and more convoluted. Firstly to generate a stable cutting condition, and ultimately for an operator to be able to understand the cutting conditions, when all normal senses to interpret the machining stability are removed. The aim for the research is, to enable the operation of high slenderness ratio internal milling operations to become a viable technology, by detailing the measures required, to obtain a stable cutting condition. The process needs to be monitored for degradation of the tooling due to wear, and to prevent catastrophic machine damage from tool breakage or machine component failure. This research addresses the lack of knowledge available for milling with extended reaches, and the knowledge gained to overcome the real difficulties that exist for this process. Initial experiments are conducted on a prototype machine to gain experience of the internal machining operation and the many issues that it faced. Establishing requirements of the process via investigation of the tooling and necessary auxiliary equipment, it becomes possible to consider countermeasures to address the errors generated by torsional twisting of the milling arm. A system for applying a counter torque to reduce torsional deflection errors has been employed to successfully reduce the unavoidable issue over such long distances. For the process to become manageable for an industrial operator without a high level of specialist knowledge, the application of tool condition monitoring (TCM) and process condition monitoring (PCM) had to be applied. This addresses a void in available literature and research with respect to internal machining, and enables the process to become practical for an industrial environment. For this reason the research project will concentrate on the application of TCM and PCM onto the machining system. The completion of the research resulted in the process becoming satisfyingly stable, and with a resulting accuracy that satisfies the requirements of the component. Performance of the final system rivalled or achieved better results than had been experienced by the project sponsor.
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Quaranta, Giacomo. "Efficient simulation tools for real-time monitoring and control using model order reduction and data-driven techniques." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667474.
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Numerical simulation, the use of computers to run a program which implements a mathematical model for a physical system, is an important part of today technological world. It is required in many scientific and engineering fields to study the behaviour of systems whose mathematical models are too complex to provide analytical solutions and it makes virtual evaluation of systems responses possible (virtual twins). This drastically reduces the number of experimental tests for accurate designs of the real system that the numerical model represents.
However these virtual twins, based on classical methods which make use of a rich representations of the system (ex. finite element method), rarely allows real-time feedback, even when considering high performance computing, operating on powerful platforms. In these circumstances, the real-time performance required in some applications are compromised. Indeed the virtual twins are static, that is, they
are used in the design of complex systems and their components, but they are not expected to accommodate or assimilate data so as to define dynamic data-driven application systems. Moreover significant deviations between the observed response and the one predicted by the model are usually noticed due to inaccuracy in the employed models, in the determination of the model parameters or in their time evolution.
In this thesis we propose different methods to solve these handicaps in order to perform real-time monitoring and control. In the first part Model Order Reduction (MOR) techniques are used to accommodate real-time constraints; they compute a good approximation of the solution by simplifying the solution procedure instead of the model. The accuracy of the predicted solution is not compromised and efficient simulations can be performed (digital twins). In the second part Data-Driven modelling are employed to fill the gap between the parametric solution computed by using non-intrusive MOR techniques and the measured fields, in order to make dynamic data-driven application systems, DDDAS, possible (Hybrid Twins).La simulación numérica, el uso de ordenadores para ejecutar un programa que implementa un modelo matemático de un sistema físico, es una parte importante del mundo tecnológico actual. En muchos campos de la ciencia y la ingeniería es necesario estudiar el comportamiento de sistemas cuyos modelos matemáticos son demasiado complejos para proporcionar soluciones analíticas, haciendo posible la evaluación virtual de las respuestas de los sistemas (gemelos virtuales). Esto reduce drásticamente el número de pruebas experimentales para los diseños precisos del sistema real que el modelo numérico representa. Sin embargo, estos gemelos virtuales, basados en métodos clásicos que hacen uso de una rica representación del sistema (por ejemplo, el método de elementos finitos), rara vez permiten la retroalimentación en tiempo real, incluso cuando se considera la computación en plataformas de alto rendimiento. En estas circunstancias, el rendimiento en tiempo real requerido en algunas aplicaciones se ve comprometido. En efecto, los gemelos virtuales son estáticos, es decir, se utilizan en el diseño de sistemas complejos y sus componentes, pero no se espera que acomoden o asimilen los datos para definir sistemas de aplicación dinámicos basados en datos. Además, se suelen apreciar desviaciones significativas entre la respuesta observada y la predicha por el modelo, debido a inexactitudes en los modelos empleados, en la determinación de los parámetros del modelo o en su evolución temporal. En esta tesis se proponen diferentes métodos para resolver estas limitaciones con el fin de realizar un seguimiento y un control en tiempo real. En la primera parte se utilizan técnicas de Reducción de Modelos para satisfacer las restricciones en tiempo real; estas técnicas calculan una buena aproximación de la solución simplificando el procedimiento de resolución en lugar del modelo. La precisión de la solución no se ve comprometida y se pueden realizar simulaciones efficientes (gemelos digitales). En la segunda parte se emplea la modelización basada en datos para llenar el vacío entre la solución paramétrica, calculada utilizando técnicas de reducción de modelos no intrusivas, y los campos medidos, con el fin de hacer posibles los sistemas de aplicación dinámicos basados en datos (gemelos híbridos).
La simulation numérique, c'est-à-dire l'utilisation des ordinateurs pour exécuter un programme qui met en oeuvre un modèle mathématique d'un système physique, est une partie importante du monde technologique actuel. Elle est nécessaire dans de nombreux domaines scientifiques et techniques pour étudier le comportement de systèmes dont les modèles mathématiques sont trop complexes pour fournir des solutions analytiques et elle rend possible l'évaluation virtuelle des réponses des systèmes (jumeaux virtuels). Cela réduit considérablement le nombre de tests expérimentaux nécessaires à la conception précise du système réel que le modèle numérique représente. Cependant, ces jumeaux virtuels, basés sur des méthodes classiques qui utilisent une représentation fine du système (ex. méthode des éléments finis), permettent rarement une rétroaction en temps réel, même dans un contexte de calcul haute performance, fonctionnant sur des plates-formes puissantes. Dans ces circonstances, les performances en temps réel requises dans certaines applications sont compromises. En effet, les jumeaux virtuels sont statiques, c'est-à-dire qu'ils sont utilisés dans la conception de systèmes complexes et de leurs composants, mais on ne s'attend pas à ce qu'ils prennent en compte ou assimilent des données afin de définir des systèmes d'application dynamiques pilotés par les données. De plus, des écarts significatifs entre la réponse observée et celle prévue par le modèle sont généralement constatés en raison de l'imprécision des modèles employés, de la détermination des paramètres du modèle ou de leur évolution dans le temps. Dans cette thèse, nous proposons di érentes méthodes pour résoudre ces handicaps afin d'effectuer une surveillance et un contrôle en temps réel. Dans la première partie, les techniques de Réduction de Modèles sont utilisées pour tenir compte des contraintes en temps réel ; elles calculent une bonne approximation de la solution en simplifiant la procédure de résolution plutôt que le modèle. La précision de la solution n'est pas compromise et des simulations e caces peuvent être réalisées (jumeaux numériquex). Dans la deuxième partie, la modélisation pilotée par les données est utilisée pour combler l'écart entre la solution paramétrique calculée, en utilisant des techniques de réduction de modèles non intrusives, et les champs mesurés, afin de rendre possibles des systèmes d'application dynamiques basés sur les données (jumeaux hybrides).
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Augste, Jan. "Využití technologie virtuální reality k monitoringu a vizualizaci vybraných provozních vlastností stroje a jejich analýze." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-319154.
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This dissertation deals with the study of the use of virtual technologies for an analysis of operating characteristics of a machine. Operating characteristics of a machine are parameters that determine the usability of the machine to accomplish a certain task in addition to the specified time and under the certain conditions. These include parameters that can be changed based on customer requirements, for example, the table size and the highest spindle speed to the parameters influenced from the start of the machine design such as positioning accuracy, acceleration value in the axes or consumption. The first part sets out general requirements for visualization of information and these are subsequently verified experimentally in the chapter devoted to the time visualization protocol. This thesis presents the original experiments to determine the basic requirements for visualization of a measured quantity, in this case the feed rate. By verifying usability when displaying these properties on the underlying data showing machine position change, the four basic requirements for visual data processing using immerse virtual reality are validated. A deeper insight into the work with data will enable to analyse more complicated relations within a longer period, greater transparency and clearer outputs of analysis.
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Lam, Bao Hoai. "Sensors and wireless networks for monitoring climate and biology in a tropical region of intensive agriculture : methods, tools and applications to the case of the Mekong Delta of Vietnam." Thesis, Brest, 2018. http://www.theses.fr/2018BRES0006/document.
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Les changements climatiques ont des impacts considérables sur le temps, les océans et les rivages, la vie sauvage. Ils amènent des problèmes désormais considérés comme majeurs par les gouvernements et organisations internationales. Ces efforts ont fourni un cadre à cette thèse, qui propose de procéder en boucle fermée de l’observation d’insectes ravageurs, avec des centaines de capteurs en réseau ("light traps"), au système d’information, et enfin à des décisions de lutte, manuelles ou automatiques. Le point d’appui pratique est la conception d’un système de comptage d’insectes proliférant dans les cultures de riz (BPH). L’abstraction que nous développons est celle d’une machine environnementale de grande taille, distribuée, qui capte et synthétise l’information, élabore des connaissances, et prend des décisions. Autour de cette abstraction, nous avons élaboré un système de vision "fisheye" effectuant le comptage des insectes. Nous proposons un système d’information géographique directement connecté au réseau de capteurs. Le couplage direct, "cyber-physique", entre les systèmes d’information et l’observation de l’environnement à échelle régionale est une nouveauté transposable, qui permet de comprendre et contrôler quantité d’évolutionsClimate changes bring problems related to nature evolutions. Global warming has an impact on sea level, weather patterns, and wild life. A number of national and international organizations are developing research programs in these directions, including threats on cultures and insect proliferation. Monitoring these phenomena, observing consequences, elaborating counteracted strategies are critical for the economy and society.The initial motivation of this work was the understanding of change impacts in the Mekong Delta region. From there, automatic observation tools were designed with a real time information system able to integrate environmental measures, then to support knowledge production.Tracking environment evolutions is distributed sensing, which can be the association of efficient sensors and radio communications, operated under the control of an information system. Sensing insects is very complex due to their diversity and dispersion. However, this is feasible in the case of intensive agricultural production as it is the case of rice, having a small number of pests. An automatic vision observatory is proposed to observe the main threats for the rice, as an evolution of manual light traps. Radio communication weaves these observatories into a network with connection to databases storing measures and possible counteractions. An example observatory has a fisheye camera and insect counting algorithms for the BPH practical case in Vietnam.By considering the observation system as an input for an abstract machine, and considering decision and actions taken as a possible control on the environment, we obtain a framework for knowledge elaboration that can be useful in lots of other situations
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Di, Corpo Jeffrey. "Predictive monitoring for metal cutting machine tools." 1990. http://catalog.hathitrust.org/api/volumes/oclc/23037426.html.
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Pereira, Sofia Perestrelo de Vasconcelos Cardoso. "Remote Sensing and Machine Learning Tools for Vegetation Monitoring." Dissertação, 2020. https://hdl.handle.net/10216/128094.
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Pereira, Sofia Perestrelo de Vasconcelos Cardoso. "Remote Sensing and Machine Learning Tools for Vegetation Monitoring." Master's thesis, 2020. https://hdl.handle.net/10216/128094.
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Scheffer, Cornelius. "Monitoring of tool wear in turning operations using vibration measurements." Diss., 2000. http://hdl.handle.net/2263/30483.
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This study investigates the use of vibration and strain measurements on machine tools in order to identify the propagating wear of the selected tools. Two case studies are considered, one of which was conducted in the plant of a South African piston manufacturer. The purpose of the ftrst case study was to investigate the feasibility of vibration monitoring to identify tool wear, before attempting to implement a monitoring system in the manufacturing plant. During this case study, data from a turning process was recorded using two accelerometers coupled to a PL202 real time FFT analyser. Features indicative of tool wear were extracted from the sensor signals, and then used as inputs to a Self-Organising Map (SOM). The SOM is a type of neural network based on unsupervised learning, and can be used to classify the input data into regions corresponding to new and worn tools. It was also shown that the SOM can also be used very efficiently with discrete variables. One of the main contributions of the second case study was the fact that a unique type of tool was investigated, namely a synthetic diamond tool specifically used for the manufacturing of aluminium pistons. Data from the manufacturing of pistons was recorded with two piezoelectric strain sensors and a single accelerometer, all coupled to a DSPT Siglab analyser. A large number of features indicative of tool wear were automatically extracted from different parts of the original signals. These included features from time and frequency domain data, time series model coefficients as features and features extracted from wavelet packet analysis. A correlation coefficient approach was used to auto-lJUltically select the best features indicative of the progressive wear of the diamond tools. The SOM was once again used to identify the tool state. Some of the advantages of using different map sizes on the SOM were also demonstrated. A near 100% correct classification of the tool wear data was obtained by training the SOM with two independent data sets, and testing it with a third independent data set. It was also shown that the monitoring strategy proposed in the second case study can be fully automated and can be implemented on-line if the manufacturer wishes to. Some of the contributions of this study are the use of the SOM for tool wear classification, and conclusions regarding the wear modes of the synthetic diamond tools.Dissertation (M Eng (Mechanical Engineering))--University of Pretoria, 2006.
Mechanical and Aeronautical Engineering
unrestricted
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Vuza, Simo S. "A systematic approach in product development of industrial processing equipment." Thesis, 2013. http://hdl.handle.net/10210/8689.
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M.Phil. (Electrical & Electronic Engineering Science)The need to industrialise South Africa has been an effort of government to increase manufacturing and Gross Domestic Products (GDP) while also creating decent work. Manufacturing industry has been striking with organisations closing and moving. Organisations have been established and fail to compete in the market due to lack of expertise to produce products that meet the customer`s requirements. Due to the opportunities of industrialisation in Africa the focus to develop equipment for these industries is necessary. This research objective is to develop a Systematic Approach of Product Development for Industrial Processing Equipment manufacturers that supply various organisations. The research will respond to the following question while also defining the development process:- Will product development be helpful in industrialising South Africa and building sustainable manufacturing businesses? Define a process feedback diagram of a systematic approach of product development to be used by industrial processing equipment? The research is done with the use of literature review form published sources which is validated by survey questions that were sent to sustainable successful organizations that supply industrial equipment. The research finding demonstrates the success phases and steps to be followed when developing product. The phases and their steps are;- 1. Marketing phase stages are Identifying Market Opportunities, Evaluating Potential Markets, Identifying Customer`s Needs and Product specification 2. Concept Development phase stages are system engineering for requirements identification & allocations, Internal & external Search of solution, concept selection and concept testing 3. System Level Design phase stages is Product Architecture 4. Detailed Design Phase stages are industrial design and design for manufacturing 5. Refinement and testing phase stages are designing for reliability, prototyping and testing. These are the phases the research focused on. The Survey revealed that success sustainable organisation have been using product development, marketing and system engineering methodologies as one of their common weapon to stay in business and grow in today’s competitive market place. This systematic approach process feedback diagram in product development has a lot of phase overlap. All phases interact even though there is still a feed process from one phase to the next. The process happens concurrently to ensure that all stages are considered at an earlier stage
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Dye, Steven. "Computer integration of modeling and monitoring systems for machine tools." 1994. http://catalog.hathitrust.org/api/volumes/oclc/33018869.html.
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Thesis (M.S.)--University of Wisconsin--Madison, 1994.Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 74-76.
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Nickel, Thomas. "Estimation of physical parameters in mechanical systems for predictive monitoring and diagnosis." Thesis, 1999. http://hdl.handle.net/1957/33479.
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Monitoring, diagnosis and prediction of failures play key roles in automatic
supervision of machine tools. They have received much attention because of the
potential for reduced maintenance expenses, down time, and an increase in the
equipment utilization level. At present, signal analysis techniques are predominantly
used. But methods involving system analysis are capable of providing more reliable
information, especially for predictive applications of supervision. System analysis
involves comprehensive analytical models combined with techniques developed in
control theory, and experimental modal analysis.
The primary objective of this research is to develop a methodology to monitor
critical physical parameters of mechanical systems, which are difficult to measure
directly. These parameters are inherent features of constitutive rigid body models. A
method for computer aided model generation developed in this thesis leads to a gray
box model structure by which physical parameters can be estimated from experimental
data. Lagrange's energy formalism, linear algebra and homogenous transformations
are used to promote parsimonious three-dimensional model building. A software
environment allowing symbolic and arbitrary precision computations facilitates
efficient mapping of physical properties of the actual system into specific quantities of
the analytical model.
Six different methods are postulated and analyzed in this thesis to estimate
physical parameters such as masses, stiffnesses and damping coefficients.
Implementation of this methodology is a prerequisite for the design of an on-line
monitoring and diagnosis system, which can detect and predict process faults. Two
mechanical systems are used to validate the proposed methods: (1) A simple multi
degree-of-freedom (MDOF) system and (2) a machine tool spindle assembly.
A practical application of physical parameter estimation is proposed for
preload monitoring in high-speed spindles. Preload variations in the bearing can lead
to thermal instability and bearing seizure. The feasibility of using accelerometers
located on the spindle housing to estimate bearing preload is evaluated.
The optimal environment for continuation of this research is collaboration with
machine tool companies to incorporate the proposed methodology (or parts of it) into
current design practices.Graduation date: 1999
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Chen, Kuan-Wen, and 陳冠文. "Development of Tool Condition Detection and Monitoring System for Machine Tools." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/11987701472260557261.
Full textAbstract:
碩士臺灣大學
機械工程學研究所
98
It is well known that tool condition monitoring system plays an important role in automatic machining system. By detecting, and thus changing the worn tool in time, the loss due to defect products can be greatly reduced and hence ensuring product quality and reliability. The purpose of this research is to develop a tool condition detection and monitoring system for the tool wear and breakage during cutting process. According to many research findings, the characteristic frequency energy of cutting vibration signal gives the best information corresponding to the tool condition. However, in order to isolate the characteristic frequencies, it requires selecting appropriate filters that are difficult for the less-skilled operators in applications of the Fourier based methods. To avoid this difficulty, spectrum correlation method is proposed in this study. The experimental results showed that the spectrum correlation method was able to detect the tool wear and chipping, but it is unable to find out the chipping position of the worn tool. For this reason, further investigation was made to the cutting forces. The experimental results showed that cutting forces increase sharply right after the tool chipping zone was engaged into the workpiece. A rapid change in cutting forces can itself be a good indicator to detect the tool failure. According to the force features received, the tool chipping detection and monitoring system, that has the capacity to recognize the chipping position of cutting tool, was developed. Experimental verification was conducted with a high degree of success.
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Wei, Shu-Yun, and 魏書筠. "A Study and Development of Machine Tools Monitoring and Demonstration System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/72925005595989321344.
Full textAbstract:
碩士實踐大學
資訊科技與管理學系碩士班
103
In today’s advanced manufacturing industries, machine tools are used to manufacture metal parts for various industries such as aerospace, military, machinery industry and multiple others. According to Gardner Research (2015), Taiwan has maintained a high production of competiveness in machine tools manufacturing, but did Korea, Japan and China as well. Thus, Taiwan has emphasized its importance in maintaining its global competiveness in the machine tools industry. Currently, Taiwan's machine tools industries advertise via a traditional method of marketing by using static videos and leaflets. In addition, numerous workers have to operate each machine tool individually with various production rates. Thus, to enhance Taiwan’s stance amongst its competitors, it is necessary to integrate high-tech technology into the machine tools industries. After the proposal of Industry 4.0, several manufacturing industries began developing unmanned and automated factories. Along with global industrial upgrading via the creation of IoT (Internet of Things), cloud computing, and wearable technology, has led to an increase in convenience, manageability, and accessibility of accessing information immediately. Thus, the need to create a system that integrates smart factories and technology is demonstrated via the machine tools monitoring and demonstration system (MTMD system). By utilizing the MTMD system via wearable technology with Augmented Reality (AR) and Virtual Reality (VR), will allow industries to monitor and manage their production level in real-time with ease and efficiency. Thus, the implementation of the MTMD system will greatly enhance Taiwan's competitive role in the machine tools industry worldwide. In addition, the MTMD includes adopting AR and VR in marketing, where potential clientele will not only be able to observe the details of various machine tools in 3D in AR, but will also be able to see how each machine tool operates in 3D animation. In addition, viewing products in AR & VR will bring Taiwan’s machine tools industries into the limelight. MTMD also encompasses the combination of Google Glass to access each machine tool’s data by creating an automated monitoring and maintenance system. By adopting an indoor localization technology iBeacon, MTMD is able to collect and access data in cloud from various machine tools via in an energy efficient manner immediately. In addition, MTMD allows each operator to analyze and monitor the production level of each machine tool and make immediate adjustments if necessary of maintenance or production enhancement. Thus, the induction of MTMD will aid in achieving real-time maintenance as well as production levels of machine tools in the machine tools industries of Taiwan.
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Rybarczyk, Paul Richard. "An integrated system for the computer aided monitoring and diagnosis of machine tools." 1989. http://catalog.hathitrust.org/api/volumes/oclc/20032613.html.
Full textAbstract:
Thesis (M.S.)--University of Wisconsin--Madison, 1989.Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 143-145).
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Liang, Ming-Wei, and 梁明偉. "The chatter and temperature monitoring and control system design for the machine tools." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/91996635354586500574.
Full textAbstract:
碩士元智大學
機械工程學系
104
This thesis is to study the chatter and temperature monitoring and control in machine tools in order to increase the processing accuracy and reduce thermal deformation in machines. The first part of this paper is to study the chatter signal acquisition analysis. In the past, chatter suppression usually adjusts the spindle speed or reduces the amount of cutting. However, the spindle speed adjustment, mostly by trial and error and experience to prevent the occurrence of chatter, has obviously out of date, so understanding chatter cause of occurrence and the establishment of the chatter suppression tactics via the use of mechanical and electrical integration of the way is the current trend. Japanese machine tool company OKUMA has developed systems to avoid the occurrence of chatter in the product, called Machining Navi. Its principle is to use sound to measure the size and frequency of occurrence of the phenomenon of chatter to determine and adjust the speed of the spindle speed to avoid the occurrence of chatter. Therefore, this study hope to use a microcontroller with appropriate measuring chatter occurrences, and control its spindle speed in order to avoid the occurrence of chatter. Finally, the original surface roughness of Rmax around 12.5S has been reduced to around 3.2S. The second part of this thesis is to study thermal deformation due to temperature effects for machine tools. During machining process, the inside and outside temperature changes of the machines have subtle effects on the machining precision. These subtle changes will show the impact of the processing stability of machine tools, and also determine the value of a machine tool. So, this part is measuring the temperature change of the machine after the operation via the installation of a temperature sensor. After analyzing the data, it can make compensations by the controller. In order to evaluate this compensation to the machine tools, we measure it displacement to test its performance. In the thermal displacement studies, through changes in the external value of the displacement method to test thermal displacement compensation, the current result is the total displacement can be controlled within 0.003mm. In summary, we have designed a monitoring and control system for chatter and temperature compensated in machine tools. This system can be obtained into much better processing accuracy when we use more advanced monitoring and control algorithms in the near future.
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CHANG, CHIA-HAO, and 張嘉豪. "Thermal Deformation Measurement of Rotary Axes and Development of Remote Monitoring Platform for Machine Tools." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/74kfnb.
Full textAbstract:
碩士國立虎尾科技大學
機械與電腦輔助工程系碩士班
107
Due to the rapid development of the machinery industry, the processing technology of five-axis machine tools is be improved every day. The requirements of machining accuracy are more and higher. However, thermal errors of machine tools are one of the major sources of inaccuracy. The thermal deformation compensation has been paid attention to the industry in recent years. Accordingly, reducing thermal deviation is an indispensable problem in the future. The thermal deformation errors of rotary axes of the five-axis machine tool are measured in this study. The inspection methods for the thermal deformation of machine tools of the spindle and linear axis have international standard specifications. However, the inspection methods of the rotary axes thermal deformation are not included in any international standard yet. Therefore, this study refers to the references [16][21][25], propose the methods to measure the thermal deformation of the rotary axes. The content of this study is divided into three parts. The first part focuses on the measurement methods of the thermal deformation errors of the rotary axes. The measurement methods are using different instruments such as double ball bar, inductive gauging sensor, and rotary axis calibrator to measure. The second part is establishing the model for predicting the thermal displacement of rotary axes. The linear and polynomial regression analysis were used to model in this study. Finally, the remote monitoring system for machine tools was built in this study in response to the trend of Industry 4.0 and an intellectual factory. The remote monitoring system connects the controller of the machine tool by the library and communication protocols. In addition, the processing and temperature statuses were monitored by this system. At the same time, the processing information is released to the cloud platform, so that users can monitor the processing status of the machine tool by the network.
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Scheffer, Cornelius. "Development of a wear monitoring system for turning tools using artificial intelligence." Thesis, 2003. http://hdl.handle.net/2263/28640.
Full textAbstract:
Please read the abstract (Summary) in the 00front part of this documentThesis (PhD (Mechanical Engineering))--University of Pretoria, 2006.
Mechanical and Aeronautical Engineering
unrestricted
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Chan, Chih-Hao, and 陳智豪. "Study of Monitoring and Compensation System for Therr-axis Thermal Expansion Errors for CNC Machine Tools." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/85779165190607028468.
Full textAbstract:
碩士中原大學
機械工程研究所
96
This study mainly developed an error compensation system which can be applied to reduce the thermal expansion error of the spindle of a CNC machine tool. Temperature variations at 5 locations on the spindle were measured by PT-100 thermal sensors and self-designed electric circuit, and the deviations of the spindle in x, y and z directions due to thermal expansion were measured by high precision displacement sensors. Based on the measured temperatures and deviations, a regression mathematic model was calculated and implanted into the PIC chip to form a system for on-line thermal expansion monitoring system. When a machine tool is working, PT100 sensors can on-line measure the temperature variations of the spindle and feedback to the monitoring system to predict the thermal expansion of the spindle. The predicted expansions are then outputted to CNC controller for error compensation through an optical coupling circuit. The experimental results have shown that the system can effectively reduced the thermal expansion error from 40m to less than 10 m.
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Yi-YungKuo and 郭奕詠. "Monitoring of Flow Resistance and Temperature Rise of Spindle Coolant and Early Warning Criteria for Machine Tools." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6p6ngn.
Full text
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Chan, Hui-Yuan, and 詹惠媛. "Application of Fiber Bragg Grating Sensors for Machine Health Monitoring of Built-in High-speed Spindle and the Measurement of the Machine Tools." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jp8t8u.
Full text
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Govender, André. "Development of a condition monitoring philosophy for a pulverised fuel vertical spindle mill." Thesis, 2016. http://hdl.handle.net/10539/21156.
Full textAbstract:
A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science in Engineering.
Johannesburg 2016The quantity and particle size distribution of pulverised coal supplied to combustion equipment downstream of coal pulverising plants are critical to achieving safe, reliable and efficient combustion. These two key performance indicators are largely dependent on the mechanical condition of the pulveriser. This study aimed to address the shortfalls associated with conventional time-based monitoring techniques by developing a comprehensive online pulveriser condition monitoring philosophy. A steady-state Mill Mass and Energy Balance (MMEB) model was developed from first principles for a commercial-scale coal pulveriser to predict the raw coal mass flow rate through the pulveriser. The MMEB model proved to be consistently accurate, predicting the coal mass flow rates to within 5 % of experimental data. The model proved to be dependent on several pulveriser process variables, some of which are not measured on a continuous basis. Therefore, the model can only function effectively on an industrial scale if it is supplemented with the necessary experiments to quantify unmeasured variables. Moreover, a Computational Fluid Dynamic (CFD) model based on the physical geometry of a coal pulveriser used in the power generation industry was developed to predict the static pressure drop across major internal components of the pulveriser as a function of the air flow through the pulveriser. Validation of the CFD model was assessed through the intensity of the correlation demonstrated between the experimentally determined and numerically calculated static pressure profiles. In this regard, an overall incongruity of less than 5 % was achieved. Candidate damage scenarios were simulated to assess the viability of employing the static pressure measurements as a means of detecting changes in mechanical pulveriser condition. Application of the validated pulveriser CFD model proved to be highly advantageous in identifying worn pulveriser components through statistical analysis of the static pressure drop measured across specific components, thereby demonstrating a significant benefit for industrial application.
MT2016
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CHENG, YIN-CHUN, and 鄭尹俊. "Research and development of automatic monitoring technique for carriage preload degradation and adaptive spindle speed estimating techniques of machine tools." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/22994314019734070302.
Full textAbstract:
碩士國立中正大學
機械工程系研究所
104
Two important techniques of machine tool based on operational modal analysis (OMA) are proposed and realized. The first is an automatic monitoring technique for preload degradation of linear guide ways, and the second is an adaptive spindle cut-ting speed technique. The linear guideway type (LGT) recirculating linear ball bearing abrasion caused by long time operation, eventually leads to preload loss, which however often occurs much earlier than the guide ways fatigue. Therefore, detecting the preload loss be-comes an important issue especially in a machine tool designed for high speed and high accuracy. In this study, a novel methodology of monitoring degradation of linear guideway type recirculating linear ball bearing of an X-Y table is proposed. By simply attaching accelerometers on the worktable of the feed drive system and then exciting the worktable with a pulse from servo motor, the worktable natural frequencies and the corresponding mode shapes are identified based on the method of OMA. Thereaf-ter, tracking the change of yawing mode frequency of worktable using modal assur-ance criteria (MAC), the linear bearing preload degradation can be monitored auto-matically without exciting the worktable manually. The material removal rate (MRR) reflects the machining efficiency, so how to increase the MRR without inducing instability, i.e. chatter, is always an important is-sue in machining. In this study, the natural frequencies, mode shapes and correspond-ing damping ratio of spindle tool system are identified firstly by using OMA. With these information, the stability lobe diagram (SLD) which depicts the machining sta-bility in terms of spindle speed and stiffness ratio can then be created. With this SLD, the optimal spindle speed can be determined. During the machining, an adaptive op-timal spindle speed machining (AOSS) technique is proposed. That is, during the ma-chining process the dynamic characteristic of the spindle tool system and then the SLD are continuously identified and updated using OMA and then the spindle speed changes accordingly. Results show that using AOSS not only increases the MRR but also maintains a good workpiece surface roughness. Moreover, the associated spindle vibration and noise are also effectively reduced.
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Sun, Wei-Heng, and 孫偉恆. "Development of an Automated Tool Condition Monitoring System Based on the Machine Vision Method for the Inspection of Tools Used in CNC Turning Machines." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/289yb6.
Full textAbstract:
碩士國立臺北科技大學
機電整合研究所
105
This study proposes a method for the online tool condition monitoring of the tools used for the cutting processes in computer numerically controlled (CNC) turning machines. Through this study, we have developed a device for online tool condition monitoring based on the machine vision/image capture method. One of the key features of the image capture device developed through this study is that it will not be affected by contamination from the cutting environment. Wear and tear of machine tools can typically be classified into four categories: fracture, built-up edge, chipping, and severe wear of the insert tip condition of the external turning tools. Methods for inspection of wear and tear used in the past relied on manual inspection of the tool insert or on statistical data analysis of cutting history. However, these methods were unable to effectively enhance the production efficiency of automated turning manufacturing. By leveraging the rapid advances in software, hardware devices, and technology for enabling machine vision in recent years, we have developed an automated online inspection system for tool condition monitoring, which can provide information regarding wear and tear in real time without the need for manual intervention, thereby improving production efficiency. To overcome the impact of contamination in the cutting environment on the image capture device and the tool images it records and to improve the robustness of the tool condition inspection method, we performed detailed analyses using four different images. The images captured include the tool wear exposure image, the tool flank exposure image, the tool wear feature image, and the tool flank feature image. For the tool wear features that cannot be inspected easily, we designed a light source device with an inbuilt capability for auto-modulation of light intensity. This helped maintain a similarity in luminance across different tool images captured, thereby enabling the selection of the appropriate wear feature image for analysis. An automated inspection of the condition of tools was conducted online using batch test samples provided by the manufacturer of the mechanical parts to verify the feasibility of applying the machine vision system developed in this study for actual monitoring of tools in the CNC turning machines. With the rapid growth in practical applications of robotized manufacturing cells (RMCs), this study also used the same method for the inspection of the RMCs by mounting the automated tool condition inspection module on the manipulator with six degrees of freedom, and by carrying out repeated inspections of the CNC turning tools for the manufacturing cell. Thus, we verified the stability and repeatability of the automated inspection system for the RMC and validated the robustness of the offset tool image captured using the machine vision device.
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Tulomba, Willems Paulino. "Stabilization of the line of sight of a two axis gimballed gun-turret system." Thesis, 2012. http://hdl.handle.net/10210/4879.
Full textAbstract:
M.Ing.A two-axis gimbal system in the form of a pitch-roll gimbal and a motion simulating platform were developed to extend the capability of an existing ground-to-air prototype gun-turret. The objective was to stabilize the line of sight (LOS) of the gimbal system despite disturbances introduced by the motion simulating platform in real time. The main sensor used for the stabilization is a two-axis accelerometer which was mounted directly on the inner gimbal (roll gimbal) to form a direct-mass stabilization architecture. The stabilization control algorithm was designed and executed in the Labview® environment on a PC, and the accelerometer data is used to drive the two DC motors used as the actuators of this control system. The design of the motion simulating platform was based on a simplified Stewart-Platform and uses pneumatic cylinders as actuating limbs. All sensors and actuators in the motion simulating platform and the gimbal system are integrated with the National Instrument’s CompactRio® and Labview®. The result was a simple stabilization controller capable of achieving basic stabilization of the LOS. However, the hardware and software of this project are capable of more complex control algorithms and that forms the bulk of the suggestions for further studies.
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Huang, Yi-Wen, and 黃翊雯. "Study of Insert Condition Monitoring of CNC Lathe Machine Tools Through the Use of Frequency Domain Analysis and Neural Network Fusion Mechanism of Accelerometer Signals." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/kjgx8c.
Full textAbstract:
碩士國立臺北科技大學
機械工程系機電整合碩士班
106
In the turning process, the quality of the product is affected significantly by the tool condition. However, the tool condition cannot be evaluated based on only the operator’s judgment as it may be incorrect, thereby leading to a deterioration in the production efficiency. In this study, we develop a system for reliable tool condition monitoring, which can be used to classify the state of inserts for built-up-edge, wear, normal, and fracture. The Taguchi L9 orthogonal table is used to test the cutting conditions of the four different insert states. Moreover, nine neural network models are established for the nine orthogonal cutting conditions. In the modelling stage, frequency domain analysis is performed on the vibration signals collected during processing. Then, the frequency segmentation range is formulated from the spectrogram to extract the amplitude characteristics within the specified frequency segmentation range. Using principal component analysis and the selected feature as the input of the neural network, the dimension of the feature vector is reduced. The outcomes enable the establishment of nine orthogonal cutting condition models that accurately predict the class of the insert state. The proposed fusion mechanism is aimed at achieving the fusion effect of the nine orthogonal cutting condition models. When the cutting conditions in the range of the orthogonal test are used for turning, the insert state estimation can be performed by the fusion mechanism. The experimental results show that the proposed accelerometer-based insert state estimating method can effectively evaluate insert statuses.
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Lu, Bo active 21st century. "Improving process monitoring and modeling of batch-type plasma etching tools." Thesis, 2015. http://hdl.handle.net/2152/30486.
Full textAbstract:
Manufacturing equipments in semiconductor factories (fabs) provide abundant data and opportunities for data-driven process monitoring and modeling. In particular, virtual metrology (VM) is an active area of research. Traditional monitoring techniques using univariate statistical process control charts do not provide immediate feedback to quality excursions, hindering the implementation of fab-wide advanced process control initiatives. VM models or inferential sensors aim to bridge this gap by predicting of quality measurements instantaneously using tool fault detection and classification (FDC) sensor measurements. The existing research in the field of inferential sensor and VM has focused on comparing regressions algorithms to demonstrate their feasibility in various applications. However, two important areas, data pretreatment and post-deployment model maintenance, are usually neglected in these discussions. Since it is well known that the industrial data collected is of poor quality, and that the semiconductor processes undergo drifts and periodic disturbances, these two issues are the roadblocks in furthering the adoption of inferential sensors and VM models. In data pretreatment, batch data collected from FDC systems usually contain inconsistent trajectories of various durations. Most analysis techniques requires the data from all batches to be of same duration with similar trajectory patterns. These inconsistencies, if unresolved, will propagate into the developed model and cause challenges in interpreting the modeling results and degrade model performance. To address this issue, a Constrained selective Derivative Dynamic Time Warping (CsDTW) method was developed to perform automatic alignment of trajectories. CsDTW is designed to preserve the key features that characterizes each batch and can be solved efficiently in polynomial time. Variable selection after trajectory alignment is another topic that requires improvement. To this end, the proposed Moving Window Variable Importance in Projection (MW-VIP) method yields a more robust set of variables with demonstrably more long-term correlation with the predicted output. In model maintenance, model adaptation has been the standard solution for dealing with drifting processes. However, most case studies have already preprocessed the model update data offline. This is an implicit assumption that the adaptation data is free of faults and outliers, which is often not true for practical implementations. To this end, a moving window scheme using Total Projection to Latent Structure (T-PLS) decomposition screens incoming updates to separate the harmless process noise from the outliers that negatively affects the model. The integrated approach was demonstrated to be more robust. In addition, model adaptation is very inefficient when there are multiplicities in the process, multiplicities could occur due to process nonlinearity, switches in product grade, or different operating conditions. A growing structure multiple model system using local PLS and PCA models have been proposed to improve model performance around process conditions with multiplicity. The use of local PLS and PCA models allows the method to handle a much larger set of inputs and overcome several challenges in mixture model systems. In addition, fault detection sensitivities are also improved by using the multivariate monitoring statistics of these local PLS/PCA models. These proposed methods are tested on two plasma etch data sets provided by Texas Instruments. In addition, a proof of concept using virtual metrology in a controller performance assessment application was also tested.