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Springer Theses

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Authors: Wuest, Thorsten

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  • 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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eBook $89.00
price for USA in USD (gross)
  • ISBN 978-3-319-17611-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.99
price for USA in USD
  • ISBN 978-3-319-17610-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $119.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: November 4, 2016
  • ISBN 978-3-319-38698-0
  • Free shipping for individuals worldwide
About this book

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)

Table of contents (8 chapters)

Buy this book

eBook $89.00
price for USA in USD (gross)
  • ISBN 978-3-319-17611-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.99
price for USA in USD
  • ISBN 978-3-319-17610-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $119.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: November 4, 2016
  • ISBN 978-3-319-38698-0
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Authors
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