Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes

Variability arises in multistage manufacturing processes (MMPs) from a variety of sources. Variation reduction demands data fusion from product/process design, manufacturing process data, and quality measurement. Statistical process control (SPC), with a focus on quality data alone, only tells half of the story and is a passive method, taking corrective action only after variations occur. Learn how the Stream of Variation (SoV) methodology helps reduce or even eliminate variations throughout the entire MMP in Jianjun Shi's Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. The unified methodology outlined in this book addresses all aspects of variation reduction in a MMP, which consists of state space modeling, design analysis and synthesis, engineering-driven statistical methods for process monitoring and root-cause diagnosis, and quick failure recovery and defect prevention. Coverage falls into five sections, beginning with a review of matrix theory and multivariate statistics followed by variation propagation modeling with applications in assembly and machining processes. The third section focuses on diagnosing the sources of variation while the fourth section explains design methods to reduce variability. The final section assembles advanced SoV-related topics and the integration of quality and reliability. Introducing a powerful and industry-proven method, this book fuses statistical knowledge with the engineering knowledge of product quality and unifies the design of processes and products to achieve more predictable and reliable manufacturing processes.

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Modeling and Analysis of Stream of variation in Multistage Manufacturing Processes

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Computational Methods and Production Engineering

Computational Methods and Production Engineering: Research and Development is an original book publishing refereed, high quality articles with a special emphasis on research and development in production engineering and production organization for modern industry. Innovation and the relationship between computational methods and production engineering are presented. Contents include: Finite Element method (FEM) modeling/simulation; Artificial neural networks (ANNs); Genetic algorithms; Evolutionary computation; Fuzzy logic; neuro-fuzzy systems; Particle swarm optimization (PSO); Tabu search and simulation annealing; and optimization techniques for complex systems. As computational methods currently have several applications, including modeling manufacturing processes, monitoring and control, parameters optimization and computer-aided process planning, this book is an ideal resource for practitioners. Presents cutting-edge computational methods for production engineering Explores the relationship between applied computational methods and production engineering Presents new innovations in the field Edited by a key researcher in the field

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Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018

This book records the new research findings and development in the field of industrial engineering, and it will serve as the guidebook for the potential development in industrial engineering and smart manufacturing. It gathers the accepted papers from the 24th International conference on Industrial Engineering and Engineering Management held at Central South University of Forestry and Technology in Changsha during May 19-20, 2018. The aim of this conference was to provide a high-level international forum for experts, scholars and entrepreneurs at home and abroad to present the recent advances, new techniques and application, to promote discussion and interaction among academics, researchers and professionals to promote the developments and applications of the related theories and technologies in universities and enterprises, and to establish business or research relations to find global partners for future collaboration in the field of Industrial Engineering. It addresses diverse themes in smart manufacturing, artificial intelligence, ergonomics, simulation and modeling, quality and reliability, logistics engineering, data mining and other related fields. This timely book summarizes and promotes the latest achievements in the field of industrial engineering and related fields over the past year, proposing prospects and vision for the further development.

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Advances in Manufacturing Technology XXXII

The urgent need to keep pace with the accelerating globalization of manufacturing in the 21st century has produced rapid advancements in technology, research and innovation. This book presents the proceedings of the 16th International Conference on Manufacturing Research incorporating the 33nd National Conference on Manufacturing Research (ICMR 2018), held in Skövde, Sweden, in September 2018. The aim of the conference is to create a friendly and inclusive environment, bringing together researchers, academics and industrialists with practical and theoretical knowledge to share and discuss emerging trends and new challenges. The book is divided into 12 parts, covering areas such as the manufacturing process; robots; product design and development; smart manufacturing; and lean, among others. Covering both cutting-edge research and recent industrial applications, the book will appeal to all those with an interest in recent advances in manufacturing technology.

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Variation Modeling Analysis and Control for Multistage Wafer Manufacturing Processes

Geometric quality variables of wafers, such as BOW and WARP, are critical in their applications. A large variation of these quality variables reduces the number of conforming products in the downstream production. Therefore, it is important to reduce the variation by variation modeling, analysis and control for multistage wafer manufacturing processes (MWMPs). First, an intermediate feedforward control strategy is developed to adjust and update the control actions based on the online measurements of intermediate wafer quality measurements. The control performance is evaluated in a MWMP to transform ingots into polished wafers. However, in a complex multistage manufacturing process, the quality variables may have nonlinear relationship with the parameters of the predictors. In this case, piecewise linear regression tree (PLRT) models are used to address nonlinear relationships in MWMP to improve the model prediction performance. The obtained PLRT model is further reconfigured to be complied with the physical layout of the MWMP for feedforward control purposes. The procedure and effectiveness of the proposed method is shown in a case study of a MWMP. Furthermore, as the geometric profiles and quality variables are important quality features for a wafer, fast and accurate measurements of those features are crucial for variation reduction and feedforward control. A sequential measurement strategy is proposed to reduce the number of samples measured in a wafer, yet provide adequate accuracy for the quality feature estimation. A Gaussian process model is used to estimate the true profile of a wafer with improved sensing efficiency. Finally, we study the multistage multimode process monitoring problem. We propose to use PLRTs to inter-relate the variables in a multistage multimode process. A unified charting system is developed. We further study the run length distribution, and optimize the control chart system by considering the modeling uncertainties. Finally, we compare the proposed method with the risk adjustment type of control chart systems based on global regression models, for both simulation study and a wafer manufacturing process.

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Proceedings of the Pacific Rim Statistical Conference for Production Engineering

This book presents the proceedings of the 2nd Pacific Rim Statistical Conference for Production Engineering: Production Engineering, Big Data and Statistics, which took place at Seoul National University in Seoul, Korea in December, 2016. The papers included discuss a wide range of statistical challenges, methods and applications for big data in production engineering, and introduce recent advances in relevant statistical methods.

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Proceedings of the 6th CIRP Sponsored International Conference on Digital Enterprise Technology

This Proceedings volume contains articles presented at the CIRP-Sponsored Inter- tional Conference on Digital Enterprise Technology (DET2009) that takes place December 14–16, 2009 in Hong Kong. This is the 6th DET conference in the series and the first to be held in Asia. Professor Paul Maropoulos initiated, hosted and chaired the 1st International DET Conference held in 2002 at the University of D- ham. Since this inaugural first DET conference, DET conference series has been s- cessfully held in 2004 at Seattle, Washington USA, in 2006 at Setubal Portugal, in 2007 at Bath England, and in 2008 at Nantes France. The DET2009 conference continues to bring together International expertise from the academic and industrial fields, pushing forward the boundaries of research kno- edge and best practice in digital enterprise technology for design and manufacturing, and logistics and supply chain management. Over 120 papers from over 10 countries have been accepted for presentation at DET2009 and inclusion in this Proceedings volume after stringent refereeing process. On behalf of the organizing and program committees, the Editors are grateful to the many people who have made DET2009 possible: to the authors and presenters, es- cially the keynote speakers, to those who have diligently reviewed submissions, to members of International Scientific Committee, Organizing Committee and Advisory Committes, and to colleagues for their hard work in sorting out all the arrangements. We would also like to extend our gratitude to DET2009 sponsors, co-organizers, and supporting organizations.

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Statistical and Computational Techniques in Manufacturing

In recent years, interest in developing statistical and computational techniques for applied manufacturing engineering has been increased. Today, due to the great complexity of manufacturing engineering and the high number of parameters used, conventional approaches are no longer sufficient. Therefore, in manufacturing, statistical and computational techniques have achieved several applications, namely, modelling and simulation manufacturing processes, optimization manufacturing parameters, monitoring and control, computer-aided process planning, etc. The present book aims to provide recent information on statistical and computational techniques applied in manufacturing engineering. The content is suitable for final undergraduate engineering courses or as a subject on manufacturing at the postgraduate level. This book serves as a useful reference for academics, statistical and computational science researchers, mechanical, manufacturing and industrial engineers, and professionals in industries related to manufacturing engineering.

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Service Orientation in Holonic and Multi Agent Manufacturing

This book gathers the peer-reviewed papers presented at the seventh edition of the international workshop "Service Orientation in Holonic and Multi-Agent Manufacturing - SOHOMA'17", held on October 19-20, 2017 and organized by the University of Nantes, France in collaboration with the CIMR Research Centre in Computer Integrated Manufacturing and Robotics at the University Politehnica of Bucharest, Romania, the LAMIH Laboratory of Industrial and Human Automation Control, Mechanical Engineering and Computer Science at the University of Valenciennes and Hainaut-Cambrésis, France and the CRAN Research Centre for Automatic Control, Nancy at the University of Lorraine, France. The main objective of SOHOMA'17 was to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions addressing trends in service orientation of agent-based control technologies with distributed intelligence. The book is organized in eight parts, each with a number of chapters describing research in current domains of the digital transformation in manufacturing and trends in future service and computing oriented manufacturing control: Part 1: Advanced Manufacturing Control, Part 2: Big Data Management, Part 3: Cyber-Physical Production Systems, Part 4: Cloud- and Cyber-Physical Systems for Smart and Sustainable Manufacturing, Part 5: Simulation for Physical Internet and Intelligent & Sustainable Logistics Systems, Part 6: Formal Methods and Advanced Scheduling for Future Industrial Systems, Part 7: Applications and Demonstrators, Part 8: Production and Logistic Control Systems. The contributions focus on how the digital transformation, such as the one advocated by "Industry 4.0" or "Industry of the future" concepts, can improve the maintainability and the sustainability of manufacturing processes, products, and logistics. Digital transformation relates to the interaction between the physical and informational worlds and is realized by virtualization of products, processes and resources managed as services.

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