Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Authors: Wuest, Thorsten
Free Preview- Nominated as an outstanding thesis by Universität Bremen, Germany
- Reports on a simple and efficient supervised machine learning approach for the analysis and control of complex, multi-stage manufacturing systems
- Describes the implementation of a holistic machine-learning based approach for dealing with incomplete information and complex tasks in realistic manufacturing situations
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- About this book
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The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical and semiconductor). The results, which are reported in detail in this book, confirmed that it is possible to incorporate implicit process intra- and interrelations on both a process and programme level by applying SVM-based feature ranking. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control. Importantly, the method is neither limited to specific products, manufacturing processes or systems, nor by specific quality concepts.
- Table of contents (8 chapters)
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Introduction
Pages 1-13
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Developments of Manufacturing Systems with a Focus on Product and Process Quality
Pages 15-49
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Current Approaches with a Focus on Holistic Information Management in Manufacturing
Pages 51-67
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Development of the Product State Concept
Pages 69-124
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Application of Machine Learning to Identify State Drivers
Pages 125-152
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Table of contents (8 chapters)
- Download Sample pages 2 PDF (854.7 KB)
- Download Table of contents PDF (1.5 MB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
- Authors
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- Thorsten Wuest
- Series Title
- Springer Theses
- Copyright
- 2015
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-17611-6
- DOI
- 10.1007/978-3-319-17611-6
- Hardcover ISBN
- 978-3-319-17610-9
- Softcover ISBN
- 978-3-319-38698-0
- Series ISSN
- 2190-5053
- Edition Number
- 1
- Number of Pages
- XVIII, 272
- Number of Illustrations
- 129 b/w illustrations, 10 illustrations in colour
- Topics
