Intelligent e-monitoring of plastic injection molding machines.

Lau Hau Yu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 79-83).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.ivTable of Contents --- p.viChapter Chapter 1: --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Objective --- p.4Chapter Chapter 2: --- Literature Survey --- p.6Chapter 2.1 --- Plastic Injection Molding Process --- p.6Chapter 2.2 --- Monitoring and Diagnosis Methods --- p.10Chapter 2.3 --- Remote Monitoring --- p.12Chapter Chapter 3: --- Monitoring Methods --- p.15Chapter 3.1 --- Predict nozzle pressure and part weight using the Radial Basis Function Neural Network --- p.15Chapter 3.1.1 --- Motivation --- p.15Chapter 3.1.2 --- Background --- p.15Chapter 3.1.3 --- Hybrid RBF neural network --- p.17Chapter 3.1.4 --- Estimation of nozzle pressure --- p.21Chapter 3.1.5 --- Estimation of part weight: The two steps and one step methods --- p.22Chapter 3.2 --- Short shot Monitoring using Similarity --- p.25Chapter 3.2.1 --- Background --- p.25Chapter 3.2.2 --- The Dissimilarity Approach --- p.26Chapter 3.3 --- Parameter Resetting using Support Vector Machine (SVM) and Virtual Search Method (VSM) --- p.27Chapter 3.3.1 --- Background --- p.27Chapter 3.3.2 --- Support Vector Regression --- p.27Chapter 3.3.3 --- SVM Parameters Resetting using Virtual Search Method (VSM) --- p.31Chapter 3.4 --- Experiments and Results --- p.33Chapter 3.4.1 --- Introduction to Design of Experiment (DOE) --- p.33Chapter 3.4.2 --- Set-points selection based on Design of Experiment (DOE) --- p.34Chapter 3.4.3 --- Nozzle pressure estimation --- p.40Chapter 3.4.4 --- Part weight prediction using the One Step Method --- p.47Chapter 3.4.5 --- Similarity Monitoring using estimated nozzle pressure --- p.49Chapter 3.4.6 --- Similarity Monitoring using ram position --- p.54Chapter 3.4.7 --- Parameter Resetting using SVM and VSM --- p.61Chapter Chapter 4: --- The Remote Monitoring and Diagnosis System (RMDS) --- p.63Chapter 4.1 --- Introduction to the Remote Monitoring and Diagnosis System --- p.63Chapter 4.2 --- Starting Use of the Software --- p.65Chapter 4.3 --- Properties and Channel Settings --- p.66Chapter 4.3.1 --- Statistic Process Control (SPC) --- p.69Chapter 4.3.2 --- Settings --- p.71Chapter 4.3.3 --- Viewing the signals --- p.72Chapter 4.3.4 --- Short shot monitoring --- p.73Chapter 4.3.5 --- Data management --- p.73Chapter Chapter 5: --- Coeclusions and Future Works --- p.76References --- p.79Appendix A: Machine settings in the experiment --- p.84Appendix B: Measured part weight in the part weight prediction experiment --- p.86Appendix C: Measured part weight in the similarity monitoring experiment --- p.87Appendix D: Results of Parameters Resetting Experiment --- p.88Appendix E: List of figures --- p.89Appendix F: List of tables --- p.9

Approche hybride basée sur les machines à vecteurs de support et les algorithmes génétiques pour l'estimation des coûts de fabrication

L'estimation du coût des produits est une étape cmcialc pour les entreprises manufacturières d'aujourd'hui; surtout, en phase de conception, lorsque les conditions et les moyens de fabrication ne sont pas encore complètement connus. Pour ces raisons, il est important de fournir au concepteur les outils nécessaires en vue d'une estimation de coûts efficace, précise et adaptée aux connaissances relatives aux produits à ce stade. Dans ce travail, nous proposons une nouvelle méthode hybride d'estimation de coûts de produits basée sur les machines à vecteurs de support (communément appelées SVM) et les Algorithmes Génétiques (AG). Cet outil de l'intelligence artificielle fondé sur la théorie de l'apprentissage statistique a été choisi pour sa grande capacité d'apprentissage et de généralisation. Dans notre approche proposée, les SVM sont utilisées pour faire une approximation de la relation entre les conditions de conception et les paramètres du produit dans le cas d'estimation de coût. Les AG ont servi pour sélectionner les hyper-paramètres des SVM. En plus, et pour identifier les paramètres ou variables les plus influençant sur le coût final, nous avons fait appel aux « fuzzy curves », basées sur la théorie de la logique floue. De cette manière, nous pourrions jouer sur ces paramètres afin d'optimiser le coût final du produit. En résumé, notre approche hybride est capable d'effectuer l'estimation des coûts des produits, ainsi qu'une sélection des variables les plus pertinentes influençant sur ce dernier. Pour démontrer son potentiel et sa robustesse, une application dans le domaine de fabrication mécanique est présentée

Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding

Elektrisches Widerstandsschweißen (Englisch: Electric Resistance Welding, ERW) ist eine Gruppe von vollautomatisierten Fertigungsprozessen, bei denen metallische Werkstoffe durch Wärme verbunden werden, die von elektrischem Strom und Widerstand erzeugt wird. Eine genaue Qualitätsüberwachung von ERW kann oft nur teilweise mit destruktiven Methoden durchgeführt werden. Es besteht ein großes industrielles und wirtschaftliches Potenzial, datengetriebene Ansätze für die Qualitätsüberwachung in ERW zu entwickeln, um die Wartungskosten zu senken und die Qualitätskontrolle zu verbessern. Datengetriebene Ansätze wie maschinelles Lernen (ML) haben aufgrund der enormen Menge verfügbarer Daten, die von Technologien der Industrie 4.0 bereitgestellt werden, viel Aufmerksamkeit auf sich gezogen. Datengetriebene Ansätze ermöglichen eine zerstörungsfreie, umfassende und präzise Qualitätsüberwachung, wenn eine bestimmte Menge präziser Daten verfügbar ist. Dies kann eine umfassende Online-Qualitätsüberwachung ermöglichen, die ansonsten mit herkömmlichen empirischen Methoden äußerst schwierig ist. Es gibt jedoch noch viele Herausforderungen bei der Adoption solcher Ansätze in der Fertigungsindustrie. Zu diesen Herausforderungen gehören: effiziente Datensammlung, die dasWissen von erforderlichen Datenmengen und relevanten Sensoren für erfolgreiches maschinelles Lernen verlangt; das anspruchsvolle Verstehen von komplexen Prozessen und facettenreichen Daten; eine geschickte Selektion geeigneter ML-Methoden und die Integration von Domänenwissen für die prädiktive Qualitätsüberwachung mit inhomogenen Datenstrukturen, usw. Bestehende ML-Lösungen für ERW liefern keine systematische Vorgehensweise für die Methodenauswahl. Jeder Prozess der ML-Entwicklung erfordert ein umfassendes Prozess- und Datenverständnis und ist auf ein bestimmtes Szenario zugeschnitten, das schwer zu verallgemeinern ist. Es existieren semantische Lösungen für das Prozess- und Datenverständnis und Datenmanagement. Diese betrachten die Datenanalyse als eine isolierte Phase. Sie liefern keine Systemlösungen für das Prozess- und Datenverständnis, die Datenaufbereitung und die ML-Verbesserung, die konfigurierbare und verallgemeinerbare Lösungen für maschinelles Lernen ermöglichen. Diese Arbeit versucht, die obengenannten Herausforderungen zu adressieren, indem ein Framework für maschinelles Lernen für ERW vorgeschlagen wird, und demonstriert fünf industrielle Anwendungsfälle, die das Framework anwenden und validieren. Das Framework überprüft die Fragen und Datenspezifitäten, schlägt eine simulationsunterstützte Datenerfassung vor und erörtert Methoden des maschinellen Lernens, die in zwei Gruppen unterteilt sind: Feature Engineering und Feature Learning. Das Framework basiert auf semantischen Technologien, die eine standardisierte Prozess- und Datenbeschreibung, eine Ontologie-bewusste Datenaufbereitung sowie halbautomatisierte und Nutzer-konfigurierbare ML-Lösungen ermöglichen. Diese Arbeit demonstriert außerdem die Übertragbarkeit des Frameworks auf einen hochpräzisen Laserprozess. Diese Arbeit ist ein Beginn des Wegs zur intelligenten Fertigung von ERW, der mit dem Trend der vierten industriellen Revolution korrespondiert

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations