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Aitchison, David Robert. "Laser based cutting tool condition monitoring." Thesis, University of Hull, 1995. http://hydra.hull.ac.uk/resources/hull:3693.
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El, Siblani Ali. "Tool condition analysis and monitoring in cold rolling process." Thesis, KTH, Industriell produktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-41318.
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This research is about a costly problem in the automotive industry due to tool fracture during the splines cold rolling of steel shafts. The objective is to study the cause of this failure and propose solutions that can be implemented in the workshop.The writing starts with a brief introduction of the companies involved in shafts production and problem solving. It introduces the cold rolling process and its advantages on splines manufacturing, and it goes through relevant material and process characteristics that help to determine the cause of tool fracture.In order to understand the process failure and production flow, it has been necessary to build up an Ishikawa diagram with possible tool fracture causes. After collecting and analysing the data about the machine tool, cold rolling process and work-piece and rolling tool materials, tests and experiments have been done.It has been considered that there is a rolling tool fatigue that causes tool fracture. Beside tool fracture, two more problems with production flow instability and the right side rolling tool have been detected. Testing the material hardness of the work-piece has shown continuous hardness fluctuations from the supplier. Rolling tool misalignment has been measured by using a vernier caliper measurement device. Rolling tools material hardness analysis shows that tool is very hard and it is possible to use a tougher material which responds better to cyclic loads.Leax has tried to solve the problem by testing another lubrication and tool coatings. A modal analysis test has been performed in order to find the natural frequency of the work-piece which possibly may lead to vibration and over loading one of the rolling tools.The conclusion that has been reached is that main cause of fracture is rolling tool fatigue due to cyclic loads and it is important to use other rolling tool material. The other two detected problems, production flow instability and rigth side rolling tool fracture, should be considered as a part of the problem in order to significantly increase tools life and stabilize production flow rate.
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Seemuang, Nopparat. "Non-destructive evaluation and condition monitoring of tool wear." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13392/.
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As tool wear is unavoidable, a tool condition monitoring system is an essential system to prevent machine tool downtime due to unnecessary tool replacement, tool breakage caused by using worn tools, and to reduce part rejections. Currently, multiple indirect sensing signals are commonly fused and used to detect tool wear to enhance the system reliability and generalisation. Many of the recently developed systems uses expensive sensing methods which are seemingly not suitable for real machining. Almost all of these monitoring systems were also developed in a laboratory control environment, resulting in lower performance if they are utilised in real machining operations. This thesis takes these discrepancies as motivation to investigate and develop low-cost based tool condition monitoring systems. The cost effective tool monitoring systems were developed from a non-destructive evaluation (NDE) method and a multiple sensor fusion approach in order to monitor tool wear of common machining processes (turning, drilling, and gear hobbing). First, Barkhausen noise technique, an off-line NDT method commonly used in the hardened case-depth evaluation, was used to evaluate the coating thickness of TiN and CrN layers on HSS cutting tools. The results confirm that this proposed measurement system can be successfully used to indicate between different coating thicknesses. Secondly, an on-line tool condition monitoring system based on multiple sensor fusion using a combination of inexpensive sensors (AE, microphone, and power monitoring system) was developed. Sensory features extracted from those sensors were trained by neural networks to obtain the tool wear prediction and tool wear state classification models. The system was successfully used to predict flank wear width and classify the tool wear states during a turning operation. Furthermore, a novel sensing feature extracted from cutting sound, named 'spindle noise', was first introduced in this study as this feature can successfully detect the excessive tool wear in turning and drilling or any other machining process which has a rotary spindle. The cost-effective systems proposed in this study can be utilised in small and medium sized manufacturing companies and will improve productivity and add more value to the manufacturing processes.
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Cooper, Clayton Alan. "Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423.
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Wilcox, Anthony John. "The condition monitoring of press-working systems using ultrasonic Lamb waves." Thesis, Birmingham City University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386941.
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Zheng, Kougen. "Application of the Wigner distribution to monitoring cutting tool condition." Thesis, University of Warwick, 1992. http://wrap.warwick.ac.uk/56557/.
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This thesis is about the application of the Wigner distribution to cutting tool monitoring and control. After reviewing traditional methods, a new method is proposed. This is to regard the surface texture and geometric error of form of a machined workpiece as the fingerprint of a cutting process, to analyse it, and to extract cutting tool vibration information from it, which can then be used for cutting tool monitoring. In order to analyse the surface texture effectively, three analysing tools, i.e. the Fourier transform, the ambiguity function, the Wigner distribution (WD), are examined and compared with each other, and it is concluded that the WD is best able to analyse both stationary and nonstationary signals. Furthermore, computer simulation of both chirp signals and frequency modulated signals is then carried out, and it is shown that the WD can be used to extract useful parameters successively. In order to demonstrate the suitability of the WD for machine tool condi- tion monitoring, first cutting tool vibration are measured directly by two linear variable differential transformers mounted on the cutting tool, and then these measured data about vibration are used to verify those parameters extracted from the surface of the machined workpiece by the WD. It is found that • the extracted frequencies in both horizontal and vertical direction are within 10% of those measured, • the extracted amplitudes in both horizontal and vertical direction are highly correlated with those measured. This result confirms the feasibility of this technique. In spite of being an off-line process, this technique is simple, reliable, and can reveal the direct effect of cutting processes.
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Fu, Pan. "An intelligent cutting tool condition monitoring system for milling operation." Thesis, Southampton Solent University, 2000. http://ssudl.solent.ac.uk/1237/.
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A very important requirement of modern machining systems in an 'unmanned' factory is to change tools that have been subjected to wear or damage in time. The old tool change strategies are based on conservative estimates from the past tool wear data, hence tools can be replaced too early or too late. This can increase the production cost or endanger the quality of the products. An integrated tool condition monitoring system composed of multi-sensors, signal processing methodology and decision-making plans is a crucial requirement for automtic manufacturing processes. An intelligent tool condition monitoring system for milling operations will be introduced in this report. The system is composed of four kinks of sensors, signal transforming and sampling apparatus and a microcomputer. By using intelligent pattern recognition techniques, different sensor signals are combined and the tool wear states can be recognised reliably. The efficient and effective orthogonal experimental design procedure is applied to comprehensively verify the monitoring system in a limited number of test runs. 50 signal features are extracted from time and frequency domain and they are found to be related to the development of tool wear values. A fuzzy clustering feature filter has been developed to remove less tool wear relevant features under different cutting conditions. multi-sensor signals reflect tool condition comprehensively and the sensor fusion strategy is used to provide reliable recognition results. Combining fuzzy approaching degree and fuzzy closeness provides a unique and overall fuzzy similarity index, the two-dimensional fuzzy approaching degree. A new type of fuzzy system, the fuzzy driven neural network has been established. The network can assign signal features suitable weights to make the tool wear state recognition process more accurate and robust. The advanced B-spline neurofuzzy networds are also successfully applied in the tool condiiton monitoring process. This powerful modelling system is established by combining the qualitative fuzzy rule representation with the quantitative adaptive numeric processing process. The fuzzy driven neural network and the B-spline neurofuzzy network can then be combined to build a neurofuzzy hybrid pattern recognition system, which is more reliable and accurate. Armed with the well- developed pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for a wide range of machining conditions, robust to noise and tolerant to faults. As can be seen in the thesis, several innovations have been made in the research process of this project. The fuzzy clustering feature filter can significantly improve the efficiency and reliability of the tool wear state recognition process. The two-dimensional fuzzy approaching degree comprehensively characterises the similarity between two fuzzy sets. The fuzzy driven neural network indirectly solves the weight assignment problem of the conventional fuzzy system. The established neurofuzzy hybrid pattern recognition system obviously improves the system's recognition resolution and reliabilty
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Ajilo, Deborah (Deborah M. ). "eyeDNA : Tool Condition Monitoring for a desktop CNC milling machine." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115670.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-84).
Tool wear is a major obstacle to realizing full automation in metal cutting operations. In this thesis, we designed and implemented a low cost Tool Condition Monitoring (TCM) system using off-the-shelf sensors and data acquisition methods . Peripheral end milling tests were done on a low carbon steel workpiece and the spindle vibration, cutting zone temperature and spindle motor current were recorded. Features from these data sources were used to train decision tree models in MATLAB with the aim of classifying the stages of tool wear. Results showed that the feature sets fusing information from all data sources performed the best, classifying the tool wear stage with up to 93% average accuracy.
by Deborah Ajilo.
S.M.
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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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Dominguez, Caballero Javier Alejandro. "Live tool condition monitoring of SiAlON inserted tools whilst milling nickel-based super alloys." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/20763/.
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Cutting tools with ceramic inserts are often used in the process of machining many types of super alloys, mainly due to their high strength and thermal resistance. Nevertheless, during the cutting process, the plastic flow wear generated in these inserts enhances and propagates cracks due to high temperature and high mechanical stress. This leads to a very variable failure of the cutting tool. Furthermore, in high-speed rough machining of nickel-based super alloys, such as Inconel 718 and Waspalloy, it is recommended to avoid the use of any type of coolant. This in turn, enables the clear visualization of cutting sparks, which in these machining tasks are quite distinctive. The present doctoral thesis attempts to set the basis of a potential Tool Condition Monitoring (TCM) system that could use vison-based sensing to calculate the amount of tool wear. This TCM system would work around the research hypothesis that states that a relationship exists between the continuous wear that ceramic SiAlON (solid solutions based on the Si3N4 structure) inserts experience during a high-speed machining process, and the evolution of sparks created during the same process. A successful TCM system such as this could be implemented at an industrial level to aid in providing a live status of the cutting tool’s condition, potentially improving the effectiveness of these machining tasks, whilst preventing tool failure and workpiece damage. During this research, sparks were analyzed through various visual methods in three main experiments. Four studies were developed using the mentioned experiments to support and create a final predictive approach to the TCM system. These studies are described in each thesis chapter and they include a wear assessment of SiAlON ceramics, an analysis of the optimal image acquisition systems and parameters appropriate for this research, a study of the research hypothesis, and finally, an approach to tool wear prediction using Neural Networks (NN). To carry out some of these studies, an overall methodology was structured to perform experiments and to process spark evolution data, as image processing algorithms were built to extract spark area and intensity. Towards the end of this thesis, these spark features were used, along with measured values of tool wear, namely notch, flank and crater wear, to build a Neural Network for tool wear prediction.
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Dimla, Dimla E. "Multivariate tool condition monitoring in a metal cutting operation using neural networks." Thesis, University of Wolverhampton, 1998. http://hdl.handle.net/2436/96291.
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Silva, R. G. "Cutting tool condition monitoring of the turning process using artificial intelligence." Thesis, University of South Wales, 1997. https://pure.southwales.ac.uk/en/studentthesis/cutting-tool-condition-monitoring-of-the-turning-process-using-artificial-intelligence(25bc91ec-44fd-435b-a55d-06c564f88f35).html.
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This thesis relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. To accomplish this objective it is proposed to use a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem. With the combination of sensor-baseidn formation and inferencer ules, the result is an on-line system that can learn from experience and can update the knowledge base pertaining to information associated with different cutting conditions. Two neural networks resolve the problem of interpreting the complex sensor inputs while the Expert System, keeping track of previous successe, stimatesw hich of the two neuraln etworks is more reliable. Also, mis-classificationsa re filtered out through the use of a rough but approximate estimator, the Taylor's tool life equation. In this study an on-line tool wear monitoring system for turning processesh as been developed which can reliably estimate the tool wear under common workshop conditions. The system's modular structurem akesi t easyt o updatea s requiredb y different machinesa nd/or processesT. he use of Taylor's tool life equation, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring is, in itself, new and proved to be slightly more reliable then the Adaptive Resonance Theory neural network.
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Binsaeid, Sultan Hassan. "Multisensor Fusion for Intelligent Tool Condition Monitoring (TCM) in End Milling Through Pattern Classification and Multiclass Machine Learning." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/7.
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In a fully automated manufacturing environment, instant detection of condition state of the cutting tool is essential to the improvement of productivity and cost effectiveness. In this paper, a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach was developed to investigate the effectiveness of multisensor fusion when machining 4340 steel with multi-layer coated and multi-flute carbide end mill cutter. Feature- and decision-level information fusion models utilizing assorted combinations of sensors were studied against selected ML algorithms and their majority vote ensemble to classify gradual and transient tool abnormalities. The criterion for selecting the best model does not only depend on classification accuracy but also on the simplicity of the implemented system where the number of features and sensors is kept to a minimum to enhance the efficiency of the online acquisition system. In this study, 135 different features were extracted from sensory signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing modules. Then, these features along with machining parameters were evaluated for significance by using different feature reduction techniques. Specifically, two feature extraction methods were investigated: independent component analysis (ICA), and principal component analysis (PCA) and two feature selection methods were studied, chi square and correlation-based feature selection (CFS). For various multi-sensor fusion models, an optimal feature subset is computed. Finally, ML algorithms using support vector machine (SVM), multilayer perceptron neural networks (MLP), radial basis function neural network (RBF) and their majority voting ensemble were studied for selected features to classify not only flank wear but also breakage and chipping. In this research, it has been found that utilizing the multisensor feature fusion technique under majority vote ensemble gives the highest classification performance. In addition, SVM outperformed other ML algorithms while CFS feature selection method surpassed other reduction techniques in improving classification performance and producing optimal feature sets for different models.
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Torres, Pérez Eduardo. "Study of vibration severity assessment for Machine Tool spindles within Condition Monitoring." Thesis, KTH, Industriell produktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200928.
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Today, machine tools are indispensable for production of manufactured goods. Several industries rely in this equipment to manufacture finished products by removing material through different cutting operations. Automobile, military and aerospace are just examples of industries where machine tools are used intensively. Today these industries strive for higher precision, narrower tolerances and more productivity in order to develop higher quality products using lesser resources and minimizing the impact on the environment.Condition Based Maintenance CBM program has been proved as an effective preventive maintenance strategy to face these challenges. Reduction in downtimes, operation losses and maintenance costs are some of the benefits of adopting a CBM approach. The core of a CBM is Condition Monitoring CM, which refers to the surveillance of a suitable parameter for assessing the need of maintenance tasks in the equipment. These parameters are later compared with reference values to obtain a machine health assessment.In machine tools, vibration level in the spindle units is considered a critical parameter to evaluate machine health during their operational life. This parameter is often associated with bearing damage, imbalance or malfunction of the spindle. Despite the importance of vibration levels there is not ISO standard to evaluated spindle health. This fact obstructs in some extend the planning of maintenance task for these high precision assemblies.In the first part of this work, spindle components are studied and their function explained. Besides the main sources of vibration are listed, putting emphasis in three due to is importance when measuring vibration within condition monitoring of spindles. These are imbalance, bearing damage and critical speed. Later relevant concepts of vibration technology and signal analysis are introduced.In the experimental part of the present study, controlled experiments were carried with the purpose of understanding which factors affect vibration measurements on the spindle housing. Control variables as spindle speed, accelerometer’s angular location, and spindle position were studied. Finally, a contactless excitation device CERS and its potential for industry in detecting bearing damage, is evaluated with two experimental setups.The results indicate that vibration levels measured spindle housing depends on great extent on the angular mounting position of the accelerometers. Results also show that some vibrations severity indicators vary considerably along spindle speed range. It was also found that CERS could be potentially used on condition monitoring of machine tool spindles for detecting onset damage on bearings. However further research is considered necessary for this purpose.Idag är verktygsmaskiner nödvändiga för produktion av tillverkade varor. Flera industrier förlitar på detta utrusning för att tillverka produkter tack vare bortskärning av material i olika bearbetningsoperationer. Bild, militär och flygg-industrin är exempel av industrier där verktygsmaskiner används intensivt. Idag strävar dessa industrier för hög precision, trängre toleranser och mer produktivitet. Allt detta för att utveckla högkvalitet-produkter med mindre resurser och för att minska miljöpåverkan.Tillståndsbaserdat underhåll (Condition Based Maintenance CBM) program har bevisats som en effektiv preventiv underhåll strategi för att bemötta de nämnde utmaningar. Minskning av stopptider, slöseri i produktion och underhålls-relaterade kostnader är flera av fördelar med implementering av CBM synsätt. Kärnan bakom CBM är tillstånd övervakning (Condition Monitoring CM), vilket hänför till bevakning av en lämplig indikator för att bedöma underhållsbehov av maskinen. Dessa parameter jämförs i efterhand med referensvärden för att inhämta en bedömning av maskinens hälsa.I verktygsmaskiner, vibrationnivår i spindlar anses som en kritisk parameter för att utvärdera maskins-hälsa under dess operativt liv. Dessa parameter är ofta sammankopplad med lagerskada, obalans eller funktionsstörningar i spindeln. Trots dess betydelse, vibrationnivår i verktygspidlar är inte reglerad i form av ISO standard. Detta försvårar planeringen av underhåll för spindlarna.I första del av den här arbete, spindels olika komponenter beskrivs och dess funktion förklaras. Dessutom de vibrations huvudsorsaker listas, med fokus på tre viktigaste som är relaterad till tillstånd övervakning of spindlar. Dessa är obalans, lagerskada och kritiska varvtal. Sedan viktiga begrepp inom vibrations mättteknik och analys introduceras.I den experimentella delen av arbetet, kontrolerade tester utfördes i avsikt att förstå vilka faktorer påverkar vibrationsmätningar i spindelhuset. Kontrollvariabler som varvtal, acceloremeter vinkelställning och spindel positon undersöktes. Slutligen, utvärderas ”contactless excitation responses system” CERS samt sitt potential för att upptäcka lagerskador. Detta utfördes med två arrangemang.Resultat indikerar att vibrationsnivåer påverkas i stor utsträckning av accelerometers vinkelställning. Resultat visar också att några vibrations indikatorer varierar betydligt med spindelns varvtal. Det konstaterades också att CERS skulle kunna användas för tillståndsövervakning av verktygspindlars med syfte av upptäcka skador i spindel lager. Däremot mer forskning behövs i detta riktning.
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Berggren, Eric. "Dynamic track stiffness measurement : a new tool for condition monitoring of track substructure /." Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-341.
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Wang, Zhijun. "Investigation of a fuzzy approach to condition monitoring of tool wear during drilling." Thesis, Southampton Solent University, 1995. http://ssudl.solent.ac.uk/2418/.
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This study investigated a methodology for an on-line condition monitoring of tool wear during milling. Based on a comprehensive literature survey, a novel method called 'feature filtered fuzzy clustering' is proposed and developed. Different from the existing fuzzy clustering techniques used for the condition monitoring of machine faults and tool awear, this method can make a realistic identification and classification of tool wear under various cutting conditions by the classification of the features on which the effects of cutting conditions were removed. To realise this method, the relationships between the cutting conditions and the features under three pre-defined wear states corresponding to three clustering centres (initial, normal, severe) were established by experiemnts, which were undertaken under the conditions defined by experiemental design, and non-linear multiple regression analysis. During experiments, a sensor fusion strategy was applied in order to get information from different aspects of the milling process. A mathematical model for fuzzy clustering based on the conception of distance has been verified by experiments using inserts both with artificially created flank wear and accelerated natural flank wear during milling in a CNC tool. In order to obtain appropriate features, the effectiveness of applying different physical parameters, i.e. cutting forces, power consumption of the spindle motor, AE RMS and AE pseudo ring-down count, for monitoring of tool wear has been investigated. Employing data fusion approach and its effect on the classification results has been investigated. Also the feasibility of applying Fourier and Walsh transforms to cutting force signals during monitoring of tool wear has been investigated.
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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.
QC 20100518
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McEntee, Simon. "The application of intelligent software for on-line product quality monitoring in manufacturing processes." Thesis, Glasgow Caledonian University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295005.
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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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Wilcox, Steven John. "Cutting tool condition monitoring using multiple sensors and artificialintelligence techniques on a computer numerical controlled milling machine." Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1446.
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This work documents an investigation of the degradation of a variety of different tools whilst conducting milling operations on a computer numerical controlled (CNC) milling machine. The potential of a range of sensors to detect tool degradation has been investigated and the outputs have been incorporated into a monitoring system. Progressive degradation under nominal rough and finish face milling and rough groove milling has been investigated using a two point grooving tool and four and eight point face milling tools on En8, En24 and En24T workpiece materials. Rapid degradation of the cutting tool has also been observed under rough milling conditions using four and eight point face milling tools, whilst machining n8 and En24T materials in a variety of simulated and actual tool breakage situations. A limited investigation of the effect of the individual wear geometries associated with both progressive and instantaneous tool degradation has been conducted by simulating these geometries and carrying out rough miffing tests using a four point face milling tool on a workpiece of En8 material. Similarly, a limited investigation of the effect of machining on different machines has also been undertaken. A number of different sensing technologies have been used, including conventional sensors such as spindle current and cutting force but also novel sensing techniques such as Acoustic Emission. These have been combined using artificial intelligence techniques to provide automatic recognition of the tool wear state. Similarly, the feasibility of breakage detection/prediction has also been demonstrated.
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Chen, Xun. "A multi-physics-based approach to design of the smart cutting tool and its implementation and application perspectives." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12841.
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This thesis presents a multi-physics-based approach to the design and analysis of smart cutting tools for emerging industrial requirements, within an innovative design process. The design process is in stages according to design specifications and requires analysis, conceptual design, detailed design, prototype production and service testing. The research presented in the thesis follows the design process but focuses on the detailed design of the smart turning tool, including mechanical design, electrical wiring and sensor circuitry, embedded algorithms development, and multi-physics-based simulation for the tool system integration, design analysis and optimisation. The thesis includes the introduction of the research background, a critical literature review of the research topic, a multi-physics-based design and analysis of the smart cutting tool, a mechanical structural detail design of the prototype smart turning tool, the electrical system design focusing on cutting force measurement and embedded wireless communication features, and the final experimental testing and calibration of the smart cutting tool. The contributions to knowledge are highlighted in the conclusions chapter towards the end of the thesis. The research proposes multi-physics-based design and analysis concepts for a smart turning tool, which can measure the cutting forces on a 0.1 N scale and can also be used to monitor the tool condition, particularly for ultraprecision and micro-machining purposes. The smart turning tool is a sensored tool, constructed with wireless and plug-and-produce features. The tool design modelling and simulation was undertaken within a multi-physics modelling and analysis environment-based on COMSOL. This integrates the piezoelectric physics with mechanical structural design and radio frequency electronic communications of cutting force signals. The multi-physics simulation method takes account of all design-mechanics-physics-electronics analysis and transformations simultaneously within one computational environment, including FEA analysis, modal analysis, structural deformation, lead piezoelectric effect and wireless data/signal simulation. With the multi-physics simulation developed, the integrated design of the smart turning tool and its performance can be physically analysed and optimised in a virtual environment. The tool design process follows the total design methodology, which can be strictly executed in several design stages. Both mechanical and electrical design of the smart cutting tool are embodied into the tool detail design. The tool mechanical structure is systematically built from the selection of the tool material, through the structure analysis and further progressed with static force – strain/stress transformation, equivalent force measurement and calibration. The electrical circuitry was systematically developed from developing the customised charge amplifier, detail design of the main circuitry and coding development procedure, preliminary PCB fabrication and multi-sensor port PCB development, as well as the real-time cutting force monitoring programming and interface coding. The experiment calibrations and cutting trials with the tool system are also designed in light of the total design methodology. The experiment procedure for using the smart turning tool is further presented in two different sections. The thesis concludes with a further discussion on the main research findings, which are further supported by the highlighted contributions to knowledge and recommendations for future work.
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Xie, Tuqiang. "Development of an opto-fluidic probe for on-line noncontact dimensional inspection and tool condition monitoring in a hazardous manufacturing environment." Thesis, Brunel University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324552.
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Rosa, Simone. "Analisi dei segnali vibratori di una macchina utensile per brocciatura." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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La manutenzione predittiva è un elemento chiave dell’industria 4.0. Il monitoraggio in continuo delle condizioni di un asset infatti, si sposa alla perfezione con gli elementi fondamentali di quest’ultima quali Big Data ed Internet of Things. Nonostante la popolarità di cui gode da diversi anni non è facile trovare applicazioni concrete di manutenzione basata su condizione (Condition Based Maintenance CBM), in particolare per il monitoraggio dll’utensile nelle operazioni di broccaitura. Questo lavoro si suddivide in due sezioni principali. Nella prima parte si cerca di riassumere brevemente lo sviluppo delle strategie di manutenzione fino allo stato attuale per poi fornire un quadro della diffusione della manutenzione predittiva in ambito industriale e dei suoi benefici. Nella seconda parte viene presentata un’analisi dei segnali vibratori acquisiti da una macchina utensile per brocciatura ad elevata produttivià, al fine di verificare la fattibilità un sistema che riconosca eventuali danni all’utensile basandosi unicamente sulle vibrazioni.
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van, Der Meer Willem Arie. "A thermofluid network-based methodology for integrated simulation of heat transfer and combustion in a pulverized coal-fired furnace." Doctoral thesis, Faculty of Engineering and the Built Environment, 2021. http://hdl.handle.net/11427/33045.
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Coal-fired power plant boilers consist of several complex subsystems that all need to work together to ensure plant availability, efficiency and safety, while limiting emissions. Analysing this multi-objective problem requires a thermofluid process model that can simulate the water/steam cycle and the coal/air/flue gas cycle for steady-state and dynamic operational scenarios, in an integrated manner. The furnace flue gas side can be modelled using a suitable zero-dimensional model in a quasi-steady manner, but this will only provide an overall heat transfer rate and a single gas temperature. When more detail is required, CFD is the tool of choice. However, the solution times can be prohibitive. A need therefore exists for a computationally efficient model that captures the three-dimensional radiation effects, flue gas exit temperature profile, carbon burnout and O2 and CO2 concentrations, while integrated with the steam side process model for dynamic simulations. A thermofluid network-based methodology is proposed that combines the zonal method to model the radiation heat transfer in three dimensions with a one-dimensional burnout model for the heat generation, together with characteristic flow maps for the mass transfer. Direct exchange areas are calculated using a discrete numerical integration approximation together with a suitable smoothing technique. Models of Leckner and Yin are applied to determine the gas and particle radiation properties, respectively. For the heat sources the burnout model developed by the British Coal Utilisation Research Association is employed and the advection terms of the mass flow are accounted for by superimposing a mass flow map that is generated via an isothermal CFD solution. The model was first validated by comparing it with empirical data and other numerical models applied to the IFRF single-burner furnace. The full scale furnace model was then calibrated and validated via detailed CFD results for a wall-fired furnace operating at full load. The model was shown to scale well to other load conditions and real plant measurements. Consistent results were obtained for sensitivity studies involving coal quality, particle size distribution, furnace fouling and burner operating modes. The ability to do co-simulation with a steam-side process model in Flownex® was successfully demonstrated for steady-state and dynamic simulations.
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Hede, Brian P. "Condition monitoring of tools in CNC turning." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/14320.
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The metal cutting industry today is highly automated and, as a step towards Europe's ability to compete on the world market, an increased level of automation can be expected in the future. Therefore, much attention has been paid to the use of automated monitoring systems within the maintenance strategies designed to prevent breakdown. This research focuses on the condition monitoring of cutting tools in CNC turning, using airborne acoustic emission, (AAE). A structured approach for overcoming the problems associated with changing cutting parameters is presented with good results. A reverse and novel approach in estimating gradual tool wear in longitudinal roughing has been made by predicting cutting parameters directly from the acoustics emitted from the process. Using the RMS as a representation of the energy in the signal, where the spectral distributions are working as divisional operators, it has been possible to accurately extract a representation of feed rate, depth of cut and cutting speed from the signal. Using a simplified relationship to estimate tangential cutting force, a virtual force can be calculated and related to a certain amount of flank wear using non-linear regression. Furthermore, this research presents a monitoring solution where disturbances are eliminated by recognising the sound signatures where it, afterwards, is possible to evaluate the reliability of the wear decision. This is done by describing irregularities in the signal , where surface parameters used on a sound waveform, combined in a neural network, has been used to trigger outputs for several defined classes of disturbances. An investigation of the two wear types flank and crater wear, has been conducted and is has been concluded, that although crater wear has an effect on the AAE, it is difficult to recognise this. AAE has shown to an efficient tool to detect flank wear, where a direct relationship is shown between the changes in the cutting parameters, tool wear and AAE. This approach has resulted in a precise monitoring so lution, where flank wear can be estimated within an error of I0%.
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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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Bathe, Martin J. "On-line condition monitoring of power press tooling using ultrasonics." Thesis, Birmingham City University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316614.
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The principal objective of the research programme was to develop a technique for monitoring the condition of power-press cutting tools using ultrasonics. The principle of the technique was based upon the reflection of Surface Acoustic Waves (SAWs) from the tooling's cutting edges, and the main aim of the research was to establish the relationship between reflected SAW amplitude and tool edge condition. Three approaches were used to establish the relationship, namely on-line experiments, off-line bench-top experiments, and mathematical modelling. The modelling work involved using Transmission Line Modelling. Three types of wear were observed on the punch: i. The formation of a wear radius on the cutting edge of the punch. ii. The beating back of the bottom face of the punch adjacent to the cutting edge. iii. The formation of an abraded region on the flank of the punch adjacent to the cutting edge. The research has shown that it is possible to monitor the radius that forms on the cutting edge of the punch using the amplitude of SAW pulses reflected from the edge. Also the work has indicated that the rate of radius formation is a function of the clearance between the punch and the die. However, the effects of the change in the cutting edge's condition on the propagation delay of the SAWs, which was also investigated, was not found to be significant. Whilst in principle it should be possible to automatically monitor the condition and predict the wearing out of press-tools, the development of a working tool-moni toring system capable of monitoring the complete cutting edge of a press-tool is presently being hindered due to the lack of a suitable transducer.
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Neill, Gary David. "PC based diagnostic system for the condition monitoring of rotating machines." Thesis, Heriot-Watt University, 1998. http://hdl.handle.net/10399/1266.
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Yang, Da-Ming. "Development of novel intelligent condition monitoring procedures for rolling element bearings." Thesis, University of Aberdeen, 2001. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU151909.
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The primary aim of this thesis is to develop a novel procedure for an intelligent automatic diagnostic condition monitoring system for rolling element bearings. The applicability of this procedure is demonstrated by its implementation in a particular electric motor drive system. The novel bearing condition diagnostic procedure developed involves three stages combining the merits of advanced signal processing techniques, feature extraction methods and artificial neural networks. This procedure is the effective combination of these techniques and methods in a holistic approach to the rolling element bearing problem which provides the novelty in this thesis. Maintenance costs account for an extremely large proportion of the operating costs of machinery. In addition, machine breakdowns and consequent downtime can severely affect the productivity of factories and the safety of products. It is therefore becoming increasingly important for industries to monitor their equipment systematically in order to reduce the number of breakdowns and to avoid unnecessary costs and delays caused by repair. The rolling element bearing is an extremely widespread component in industrial rotating machinery and a large number of problems arise from faulty bearings. Therefore, proper monitoring of bearing condition is highly cost-effective in reducing operating cost. The advanced signal processing techniques used here are bispectral-based and wavelet-based analyses. The bispectral-based procedures examined are the bis-pectrum, the bicoherence, the bispectrum diagonal slice, the bicoherence diagonal slice, the summed bispectrum and the summed bicoherence. The wavelet-based procedure uses the Morlet wavelet. These methods greatly enhance the ability of an automated diagnostic process by linking the increased capability for signal analysis to the predictive capability of artificial neural networks. The bearing monitoring scheme based on bispectral analysis is shown to provide greater insight into the structure of bearing vibration signals and to offer more diagnostic information than conventional power spectral analysis. The wavelet analysis provides a multi-resolution, time-frequency approach to extract information from the bearing vibration signatures. In order to effectively interpret the wavelet map, the time-frequency domain is used instead of the time-scale domain by plotting the associated time trace and power spectrum.
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Calabrese, Francesca. "Vibration Monitoring and Intelligent Diagnosis Tools for Condition-Based Maintenance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
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Ogni impianto di produzione è caratterizzato da periodi di operatività, nei quali funziona correttamente, e da periodi di fermo, dovuti alla presenza di guasti o all’esigenza di effettuare attività volte a ristabilire il suo normale comportamento. L’obiettivo principale della funzione manutenzione è minimizzare i periodi di fermo impianto, al fine di renderlo il più disponibile possibile.
Attualmente, la manutenzione basata su condizione (CBM) è una delle più politiche più efficaci adottate dalle industrie. Essa è basata sul monitoraggio di diversi parametri della macchina che ne riflettono lo stato di salute. Tra i parametri più utilizzati si trovano i segnali di vibrazione. La CBM può essere implementata attraverso quattro passi principali: raccolta dati, analisi dei segnali, diagnostica e prognostica. Tale procedura prende il nome di Prognostic Health Monitoring (PHM).
La necessità di analizzare la grande mole di dati raccolta attraverso il vibration monitoring richiede l’utilizzo di metodi sviluppati nell’ambito della teoria statistica e del data mining, che si pongono l’obiettivo di riconoscere andamenti regolari all’interno di grandi insiemi di dati, al fine di generare conoscenza funzionale al processo decisionale manutentivo. In particolare, i modelli di classificazione, come alberi decisionali, algoritmi K-NN, reti neurali e Support Vector Machine, costituiscono un potente strumento per la diagnostica. Tali modelli, sulla base del PHM, vengono applicati dopo la fase di analisi dei segnali, che consiste principalmente nell’estrazione di features sia nel dominio del tempo che nel dominio tempo-frequenza.
Il risultato principale ottenuto consiste nell’aver verificato un incremento delle performance, in termini di accuratezza, della classificazione dello stato di salute di un componente, dovuto all’introduzione dell’analisi nel dominio tempo-frequenza e allo sviluppo dei nuovi metodi “intelligenti”.
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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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Aini, Reza. "Vibration monitoring and modelling of shaft/bearing assemblies under concentrated elastohydrodynamic condition." Thesis, Kingston University, 1990. http://eprints.kingston.ac.uk/20759/.
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A five degrees of freedom analysis of a perfect precision grinding spindle supported by a pair of back to back angular contact ball bearings is performed. The ball to race contacts are simulated by a non-linear contact spring, representing the elastic deformation of the mating rolling members. Major frequencies associated with various degrees of freedom are observed and a number of design curves, suggesting the best zones of operation for the simulated spindle under radial/ axial loading are also presented. The gyroscopic contribution of an ideal precision spindle was found to be insignificant. The model was further expanded to study the response characteristics of the spindle under lubricated contact conditions. A regression formula is used to model the non-linear spring/ damper arrangement,corresponding to the contact elastohydrodynamic oil film thickness. It is noted that the presence of the oil film along the line of contacts do not significantly alter the position of the major modes of the system. However, its contribution in damping the amplitude of oscillation are found to be significant. Various graphs indicating the overall system response, subjected to varying oil film viscosity, number of balls and the spindle mass are also presented. Furthermore, experimental investigations are conducted to validate the employed methodology. Good agreement is observed between the results of the simulation and the experimental spectra for the fundamental modes of response. Although manufacturing anamolies are not simulated,the formulated models incorporate sufficient versatility to forsee various spindle/bearing configurations, different loading arrangement as well as various geometrical features of a system to be modelled.
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Hector, Hélène. "Obstructions monitoring in sewerage pipes." Thesis, KTH, Mark- och vattenteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190608.
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When a malfunction in the collection system occurs and a pipe overflows, the wastewater may be discharged in the natural environment. To avoid such pollution, nuisances to inhabitants living nearby and extra cost for the operator, there is an issue of detecting early enough the buildup of obstructions in sewerage pipes in order to react before the damage is done. The aim of this thesis was thus to develop a decision support tool to detect obstructions and to optimize cleaning operations. Some additional specifications were the file size for sending by email, the simplicity of setup and use, the visual attractiveness and a quick visualization of results. The tool consists of two Excel files coupled with a database which permits to send a daily email to the operator with the functioning state of each measurement point. However, the tool does not do everything, human analysis is necessary to have a critical eye on the results and to decide when to trigger a cleaning operation. The main perspective at the end of this thesis is the replacement of the preventive cleaning operations that were previously performed with a fixed frequency per year by conditional cleaning operations triggered by the tool and to observe the decrease of cleaning operations. Other perspectives are to spread the tool to other sites and to use the received feedbacks to adjust the different parameters and eventually to determine an automatic trigger condition of cleaning operations.
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Khan, A. F. "Condition monitoring of rolling element bearings : a comparative study of vibration-based techniques." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292225.
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Chen, Dajun. "Applications of time-scale representations and artificial neural networks in gear condition monitoring." Thesis, De Montfort University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391569.
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Fortunet, Charles. "Zavedení systému kontroly opotřebení při vrtání a řezání závitů do strojních dílů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231706.
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The topic of this thesis is divided in two main parts. The first is about the “tool/workpiece pair” method and the second is related to wear monitoring. The entire project will be about drilling and tapping operations done in SNECMA Vernon. In fact, the part is very expensive so they have to be closely controlled to avoid a maximum scrap pieces. Two software will be used to control it. Firstly, the “tool/workpiece pair” will be done through AMC3 (software developed at the ENSAM). And secondly the wear monitoring will be ensured by the software CTM Visu (developed by ARTIS). My task will be to learn how to use those software and then to implement them in the company.
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Riato, Luisa. "Development of diatom-based monitoring tools for assessing depressional wetland condition in the Mpumalanga Highveld region South Africa." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/62567.
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Diatoms have a successful history of use in assessments of wetland biological condition. In North America and across Europe, diatom assemblages are used for routine wetland condition assessments to meet the statutory requirements of the European Water Framework Directive and the National Aquatic Resource Survey by the US Environmental Protection Agency. In South Africa, the use of diatom assemblages as indicators of wetland condition may be a promising alternative to the traditional biotic assemblages employed, such as macroinvertebrates or macrophytes, which have proven to be ineffective. We present a preliminary investigation on the feasibility of diatoms in wetland biological assessments in South Africa by evaluating the use of diatoms as indicators of biological condition for depressional wetlands in the Mpumalanga Highveld region of South Africa. Depressional wetlands typically found in this region are either temporary (seasonally inundated) or permanent depressions. Temporary depressional wetlands are expected to be affected by natural environmental disturbances (e.g., seasonal fluctuations in water-level which may cause changes in water chemistry) as compared to relatively stable permanent ones. Establishing whether diatoms are suitable indicators of natural environmental disturbances in temporary depressional wetlands in this region is necessary for further investigations of anthropogenic disturbances. We sampled epiphytic diatoms from three least human-disturbed temporary depressional wetlands during various stages of inundation and showed that the species composition of epiphytic diatom communities were strong indicators of temporally changing environmental conditions. Using the same diatom and physical and chemical data, we also demonstrated that simplifying the taxonomy by using the functional composition (ecological guilds, life-forms) of the epiphytic diatom communities, can assess temporally changing environmental conditions as effectively as the species composition. Moreover, these functional groups provide valuable ecological information that is not available from the species data. Acid mine drainage (AMD) is the predominant stressor in permanent depressional wetlands of the Mpumalanga Highveld region, where coal mines utilise these wetlands for storage of AMD, which has severe impacts on the structure and function of the ecosystem. In order to develop an approach for impact assessment and management of depressional wetlands in the region, we developed an epiphytic diatom multimetric index (MMI) for AMD impacted permanent depressional wetlands. This is also the first diatom index to quantify AMD impacts in wetland habitats. Data collected from 34 sites that represented a range of conditions along an AMD gradient within the Mpumalanga Highveld was used to select responsive diatom metrics which we combined into a multimetric index. We developed separate MMIs for classes of depressional wetland types in order to account for natural variation among diatom assemblages, and compared their performance with an MMI that did not account for natural variation. To account for natural variation, we classified reference sites based on diatom typologies and hypothesised that by using this approach, we would improve MMI performance. Overall, all MMIs performed considerably well, although grouping sites by diatom typology to account for natural variation improved MMI performance, especially the precision, responsiveness and sensitivity to disturbance. We conclude that diatoms have strong potential for use in wetland ecological assessments in South Africa. The experimental and statistical approaches used in this study should expand our knowledge of diatom ecology and further advance the research and development of diatom bioassessment.Thesis (PhD)--University of Pretoria, 2017.
Paraclinical Sciences
PhD
Unrestricted
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Demolli, Shemsi, Miriam M. Geist, Julia E. Weigand, Nicole Matschiavelli, Beatrix Süß, and Michael Rother. "Development of β-Lactamase as a Tool for Monitoring Conditional Gene Expression by a Tetracycline-Riboswitch in Methanosarcina acetivorans." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-132179.
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The use of reporter gene fusions to assess cellular processes such as protein targeting and regulation of transcription or translation is established technology in archaeal, bacterial, and eukaryal genetics. Fluorescent proteins or enzymes resulting in chromogenic substrate turnover, like β-galactosidase, have been particularly useful for microscopic and screening purposes. However, application of such methodology is of limited use for strictly anaerobic organisms due to the requirement of molecular oxygen for chromophore formation or color development. We have developed β-lactamase from Escherichia coli (encoded by bla) in conjunction with the chromogenic substrate nitrocefin into a reporter system usable under anaerobic conditions for the methanogenic archaeon Methanosarcina acetivorans. By using a signal peptide of a putative flagellin from M. acetivorans and different catabolic promoters, we could demonstrate growth substrate-dependent secretion of β-lactamase, facilitating its use in colony screening on agar plates. Furthermore, a series of fusions comprised of a constitutive promoter and sequences encoding variants of the synthetic tetracycline-responsive riboswitch (tc-RS) was created to characterize its influence on translation initiation in M. acetivorans. One tc-RS variant resulted in more than 11-fold tetracycline-dependent regulation of bla expression, which is in the range of regulation by naturally occurring riboswitches. Thus, tc-RS fusions represent the first solely cis-active, that is, factor-independent system for controlled gene expression in Archaea.
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Lin, Bor-Shiun, and 林伯恂. "Acoustic Emission Based Tool Condition Monitoring Under Different Cutting Condition." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/61850913818826122075.
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碩士國立臺灣大學
機械工程學研究所
102
Machining efficiency and quality are usually affected by the status of the machine tool and cutting conditions. A suitable cutting condition for the work piece material is very important. In order to avoid these factors that cause significant damage, the processing monitoring must be done. It’s relatively easy using a single type of sensors and signal processing during the development. However, we are monitoring the processing conditions which depend on the composition of the signal and certain physical phenomena and characteristics. To fully meet the actual situation, these signals must qualify to repeatability, reliability, responsiveness and resolution. In theory, the collection and analysis of multiple signals can get more comprehensive information. In short, sensor fusion is the solution of the demands above. The purpose of this study is to use both acoustic emission signals and cutting temperature to monitor the tool condition. By experimenting turning of 1045 steel under different cutting conditions, we are able to establish the relation between signals and tool wear thus can be extended in the case of different cutting conditions, and can estimate flank wear accurately. According to the results of this study, we can estimate if the tool wear level were within the range of appropriate processing or not with some simple cutting experiments, which provide information clearly and are easy to identify. The use of sensor fusion concepts, using acoustic emission signals and temperature detection, can identify how the acoustic emission signals, temperature, cutting speed, and wear were related, then establish mathematical models to calculate the wear level. We can estimate wear level through fewer experiments, particularly the integration of sensor fusion can effectively eliminate the impact of circumstances or inaccurate sensor failure in particular reasons caused situations.
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Richter, Frank. "On-line tool condition monitoring in milling." 1988. http://catalog.hathitrust.org/api/volumes/oclc/19712595.html.
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Thesis (M.S.)--University of Wisconsin--Madison, 1988.Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 149-156).
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"Tool Condition Monitoring and Replacement for Tubesheet Drilling." Thesis, 2013. http://hdl.handle.net/10388/ETD-2013-09-1240.
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Tool Condition Monitoring (TCM) methods have shown significant potential to automatically detect worn tools without intervention in the machining process, thus decreasing machine downtime and improving reliability and part quality. Previous research on TCM systems have used a wide variety of time-domain and frequency-domain features extracted from cutting force related parameters as well as mechanical and acoustical vibrations to infer the wear state of tools. This project concerns the process of drilling thousands of tight-tolerance holes on tubesheets and baffles of heat exchangers using large diameter indexable insert drills on a horizontal boring machine. To address the issues involved in the process, the aim of this research is to develop a non-intrusive, indirect, online TCM system on the horizontal boring machine to monitor the drill wear and hole quality while drilling. The specific objectives are to establish an indirect TCM system for the drilling process, to develop models to predict tool wear and the machining accuracy of the drilled holes, and to develop an optimum tool replacement strategy.
The TCM system developed used two cutting-force related signals on the horizontal boring machine, namely the spindle motor current and the axial feed motor current. Features extracted from these data streams, as well as the machining parameters, the cutting speed and the feed rate, and the number of holes drilled with the current inserts, are the inputs to a series of models to predict the tool wear state and the hole diameter. The first model is an autoregressive model that allows the prediction of the extracted features for the next hole before it is drilled. As each hole is drilled, this model is updated with the most recent data to improve the accuracy of the prediction. The predicted values for the features are then used as inputs to the second and third models which are surface response models, one to estimate the tool wear state and one to estimate the hole diameter.
A tool replacement strategy based on applying limits to the predicted hole diameter was also developed. Adjusting these limits allows the strategy to be tuned for either hole accuracy or tool life depending on the requirements of a specific application. Tuning the replacement strategy for tool life resulted in a significant 44% increase in tool life and a non-trivial reduction in machine down time due to fewer tool changes while holding a hole diameter tolerance of ±0.1mm. The TCM system ensured that not a single over tolerance hole would have been drilled which is critically important since over tolerance holes can result in a scrapped workpiece.
The proposed 3-model TCM system shows promise in being able to significantly reduce the risk of drilling out of tolerance holes while at the same time increasing tool life and correspondingly decreasing tool change time. The models are able to accurately predict the insert flank wear and as well as the actual hole diameter within acceptable error. The TCM system could be implemented in an industrial settingwith minimal revision and since it is an indirect system there would be no intrusion into the manufacturing operation.
One limitation of the TCM system as proposed is that it is only capable of detecting gradual tool wear and not catastrophic tool failure, a limitation that was known from the outset but was not investigated as it was beyond the scope of this project. The proposed TCM system would allow the integration of additional functionality to instantaneously detect catastrophic tool failure.
Finally, for use in a production environment, the developed models need to be implemented on a standalone device that requires essentially no operator input to monitor continuous drilling operations for tubesheet and baffle applications. This implementation could include automatic detection of the machining parameters using frequency analysis of the motor signals.
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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.
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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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Hsu, Pau-Lo. "On-line monitoring of milling cutting tool condition." 1987. http://catalog.hathitrust.org/api/volumes/oclc/18429633.html.
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Thesis (Ph. D.)--University of Wisconsin--Madison, 1987.Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 201-208).
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Teng, Wen Chih, and 鄧文治. "On Line Tool Condition Monitoring using single chip computer." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/99857122072653306381.
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碩士國立中興大學
機械工程學系
85
Abstract The purpose of this thesis is to design a "on line tool condition monitoring system using single chip microcomputer"for detecting tool breakage during cutting process.It''s well known that tool condition monitoring system plays an important role in FMS system.By change the worn tool before or just at the same time it fails,the loss of defect product can be reduced greatly and thus improve product quality、reliability . The 8051 tool monitoring system use strain gauge for measuring cutting force,according to the force feature , tool monitoring system can easily recognize the breakage of cutting tool with tool breakage algorithm. The experimental result show that the low cost 8051 tool monitoring board(about$9000) successfully detect tool breakage in threee successive products .
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Sari, Delima Yanti, and 花依婷. "Study on Tool Condition Monitoring in Micro-piercing Process." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/rc4dau.
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博士國立高雄第一科技大學
工學院工程科技博士班
106
Monitoring of micro-piercing process plays important role in insuring the optimum process, improving product quality, tooling protection and increasing productivity. In sheet metal processing, most monitoring methods use tonnage/cutting force signal. In this study, feasibility of acceleration and sound signal in the frequency range from 0 to 10000 Hz for piercing monitoring is reported. The experiments are conducted by using punch with a diameter of 0.8 mm and 1 mm. The sheet material is SUS304 with thickness of 0.5 mm. The clearances are 3%, 5%, 7% and 9% of sheet thickness. Punch speed is 50 rpm. Several series of micro-piercing experiments are conducted until the punch failure. During experiments, the vibration is measured by accelerometers which are installed on upper tool and lower tool. Sound signal is recorded by using microphone which is located about 60 mm away from the cutting point. The cutting force is also measured by mounting the force sensor above the punch. During experiments, the change of the signals, the tools condition and the burr formation are observed. The results show that by increasing number of holes, the wear progress is noticed from a dull cutting edge and the presence of burr. Slowly increase of vibrational and sound amplitude are observed, in particular, in the range of frequency which is excited by the cutting process. Feasibility of vibration signal for monitoring is investigated in frequency range of 1000 Hz and 10000 Hz. Several signal features in time domain such as kurtosis, crest factor and peak are calculated. Frequency domain features are calculated in form of energy in frequency excited by cutting process. In the range 0 to 1000 Hz, the frequency excited by cutting process is observed in frequency 250 to 700 Hz. In the range 0 to 10000 Hz, the frequency excited by cutting process in 3000 to 10000 Hz is more prominent. The increase of signal power in higher frequency range (3000 to 10000 Hz) is significant than in lower frequency (250 to 700 Hz). Prior the punch failure, signal power in frequency range excited by cutting process, displays a significant increment. The results show that the significant increment of power indicates the abnormal condition and necessity to examine the tool condition, because the developing state promotes more formation of burr or onset of tool failure due to buckling. Increase of punch load during the cutting process, clearance and additional signal resulted from the developing damage contribute to the raise of signal power. The experimental results display that the trend of vibrational power in frequency 3000 to 10000 Hz and sound power in 3000 to 4000 are in accordance with the growth of burr and the trend of punch force. By trending the power in this frequency range for number of strokes/holes, a high fluctuation of signal power indicates large variation among the strokes, while a low fluctuation indicates that variation between strokes is relatively smaller. The experiment which shows larger variation of signal power between strokes, exhibit more significant burr than experiment with smaller variation of signal power between strokes. This reveals that a low variation of signal power between strokes indicates that piercing process for every strokes is relatively stable and the tool condition is relatively better. By using vibrational data in frequency range 0 to 10000 Hz, monitoring steps are applied by employing Statistical Overlap Factor (SOF) to select the best features and logistic regression to classify the tool condition. The experimental data with 3 different clearances, i.e. 3%, 5%, and 9%, are used to examine the applicability of the proposed method. The piercing experiment with 3% clearance is employed as training data to obtain the regression coefficients (〖β_0,β〗_1,β_2…,β_k), while experimental data with 5% and 9% clearance are used as validation. Kurtosis of lower tool is found as the feature with a highest SOF’s value. Due to high correlation between the features, only 2 features are taken as predictor in logistic regression, that is kurtosis of lower tool (as representative of time domain feature) and signal power in range 3000 to 10000 Hz (as representative of frequency domain feature). The logistic models are developed with 2 ways i.e., by employing the selected feature as independent variable separately and by incorporating both selected features as independent variables. The results show that accuracy of logistic model by using signal features separately is higher than incorporated all features in one logistic model, i.e. over 99 %. The use of experimental data with 3 different clearances shows that the different clearances from 3% to 9% do not show a significant effect. Thus, in this range of clearance, the obtained logistic model is applicable. The experimental results show that the trend of signal power may reflect the trend of the process and the tool condition. The trend of signal power both vibration and sound are in accordance with the burr development and the peak punch load. All the results shows feasibility of the method for monitoring the micro-piercing process.
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Lee, Soo-Yen. "In-process tool condition monitoring systems in CNC turning operations /." 2006.
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Sung-Nien, Du, and 杜松年. "Study of the Tool Condition Monitoring with Wedge Waves using Finite Element Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/96104253766764369936.
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碩士長庚大學
機械工程研究所
95
This study based on Finite Element method to find out the reason of negative slope while the machine tool has no wearing, and use laser-based ultrasound technique measured the dispersion curve of machine tools. The basic measurement principle is the dispersion behavior of anti-symmetric flexural (ASF) mode of wedge wave influenced by the sharpness of the machine tool. The ASF mode propagating along the tool apex has a negative slope while the machine tool has no wearing. This study created models with different geometry to search the major factor influencing the slope of dispersion curves. Acorrding to the results, the arc of blade is the major factor which influencing the slope of dispersion curves.
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Hsu, Yu-Wei, and 許育瑋. "Applications of Spindle Vibration and AE Signal for Tool Condition Monitoring in Drilling." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/39631202703006183630.
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碩士國立中興大學
機械工程學系所
100
In metal cutting process, the tool wear and tool breakage will lead to decrease of machining efficiency due to the increase of machine vibration, the increase of surface roughness on finished surface, the decrease of geometry accuracy and the damage of the machine tool. Therefore, the development of the tool condition monitoring system plays an important role in developing the manufacturing automation technology. This research focus on the study of applying spindle vibration and acoustic emission (AE) signals on the tool condition monitoring in drilling. In the signal processing, the time domain signal was first transformed based on FFT and Wavelet transform to create features, followed by the feature selection based on the class mean scatter criteria to extract the feature closely related to the tool condition. Finally, the Fisher linear classifier was developed to classify the tool condition based on the selected features. To collect the data to develop and test the developed monitoring system, experiments along with two kinds of tools with diameter of 2mm and 3mm were conducted to collect the spindle vibration and AE signals in drilling 6061 Aluminum with various tool condition. Two Data acquisition boards integrated with LabView software were used for data collection. In tool wear monitoring, the effect of tool size, data collection point, and cutting parameters on the signals, as well as the effect of bandwidth size selection on the performance of tool wear monitoring system were analyzed. In tool breakage monitoring, the performance of developed system was investigated by applying to the cases with different cutting parameters from the case for system development. The results show that the spindle vibration and AE signal collected on the fixture connected to spindle housing can be used to detect tool wear and tool breakage in drilling. Moreover, the signals collected from drilling processes with various cutting parameters all demonstrate that the continuous change of feature characteristics as drilling proceed can be observed. This phenomenon might be caused by the random occurring of entangled chip or various chip/drill relationship when chip coming out of drilled hole. In the analysis of system performance in tool wear monitoring, the change of cutting parameters will change the frequency features and lead to the low classification rate when the system developed with cutting parameters differing from those for the test case. However, the model developed based on the mixed signals collected from all case with various cutting parameters will improve the classification rate dramatically. In the analysis of system performance for tool breakage monitoring, 100% classification rate can be obtained, even by developing the system based on the signals collected with cutting parameters differing from the test case.
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CHEN, PIN-NAN, and 陳品男. "Applying the Analytic Hierarchy Process to Select Sensors for Tool Condition Monitoring in Machining." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/z8vb5a.
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碩士逢甲大學
工業工程與系統管理學系
106
Over the past decade, Industry 4.0 has evolved with the great advance of instant messaging and big data analytics, proposing the concept of automation and working toward automated machine tools to improve production efficiency, product quality, and also reduce maintenance costs. The most imperative part for achieving tool automation is process monitor, which consists of two parts (1)Sensor (2)Process Monitoring System, and this research will be placed on the first part of the "sensor". A suitable sensor can satisfy the measurement frequency and accuracy required by the process monitoring system, and reduce the cost to reach the best economic efficiency. This paper will construct a model for the sensors’ key factors through the analysis hierarchical method, then compare the difference between these factors and find the sensor with the highest overall weight that allows decision makers to use as a reference.