Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Theses (Springer Theses)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
-
Front Matter
-
Back Matter
Keywords
- Holistic information management
- Holonic manufacturing systems
- Intelligent manufacturing systems
- Machine learning in manufacturing
- Manufacturing process improvement
- Manufacturing programs and processes
- Multi-stage manufacturing programmes
- PLM data
- Process and product quality
- Product data management
- Product state concept
- SVM-based feature selection
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.
Authors and Affiliations
Bibliographic Information
Book Title: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Authors: Thorsten Wuest
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-319-17611-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-17610-9Published: 04 May 2015
Softcover ISBN: 978-3-319-38698-0Published: 17 October 2016
eBook ISBN: 978-3-319-17611-6Published: 20 April 2015
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XVIII, 272
Number of Illustrations: 129 b/w illustrations, 10 illustrations in colour
Topics: Industrial and Production Engineering, Computer-Aided Engineering (CAD, CAE) and Design, Operations Management, Computational Intelligence