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

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

Softcover reprint of the original 1st Edition 2015

Paperback (17 Oct 2016)

  • $137.41
Add to basket

Includes delivery to the United States

10+ copies available online - Usually dispatched within 7 days

Publisher's Synopsis

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.

Book information

ISBN: 9783319386980
Publisher: Springer International Publishing
Imprint: Springer
Pub date:
Edition: Softcover reprint of the original 1st Edition 2015
Language: English
Number of pages: 272
Weight: 4453g
Height: 235mm
Width: 155mm
Spine width: 15mm