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

Thorsten Wuest (Autor)

Buch | Softcover

XVIII, 272 Seiten

2016 | 1. Softcover reprint of the original 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-38698-0 (ISBN)

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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.

Introduction.- Developments of manufacturing systems with a focus on product and process quality.- Current approaches with a focus on holistic information management in manufacturing.- Development of the product state concept.- Application of machine learning to identify state drivers.- Application of SVM to identify relevant state drivers.- Evaluation of the developed approach.- Recapitulation.

Erscheinungsdatum	21.10.2016
Reihe/Serie	Springer Theses
Zusatzinfo	XVIII, 272 p. 139 illus., 10 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Technik ► Maschinenbau
Schlagworte	Artificial Intelligence • Computational Intelligence • Computer-Aided Design (CAD) • Computer-Aided Engineering (CAD, CAE) and Design • Engineering • Engineering: general • Holistic information management • holonic manufacturing systems • Industrial and Production Engineering • Intelligent Manufacturing Systems • Machine learning in manufacturing • management of specific areas • Manufacturing process improvement • Manufacturing programs and processes • Multi-stage manufacturing programmes • Operations Management • PLM data • Process and product quality • Product Data Management • Production Engineering • Product state concept • SVM-based feature selection
ISBN-10	3-319-38698-0 / 3319386980
ISBN-13	978-3-319-38698-0 / 9783319386980
Zustand	Neuware