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.
| ISBN: | 9783319386980 |
| Publication date: | 17th October 2016 |
| Author: | Thorsten Wuest |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 272 pages |
| Series: | Springer Theses |
| Genres: |
Production and industrial engineering Management of specific areas Computer-aided design (CAD) Artificial intelligence |
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.
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning features in the following genres: Production and industrial engineering, Management of specific areas, Computer-aided design (CAD), Artificial intelligence
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning is available in Paperback, Hardback
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning was written by Thorsten Wuest and published by Springer an imprint of Springer International Publishing
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning has 272 pages
Yes it is part of Springer Theses series