Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning (Record no. 52867)
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000 -LEADER | |
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fixed length control field | 03472nam a22005295i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-319-17611-6 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200420221255.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 150418s2015 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783319176116 |
-- | 978-3-319-17611-6 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 670 |
100 1# - AUTHOR NAME | |
Author | Wuest, Thorsten. |
245 10 - TITLE STATEMENT | |
Title | Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XVIII, 272 p. 139 illus., 10 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | Springer Theses, Recognizing Outstanding Ph.D. Research, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | 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. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-17611-6 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2015. |
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-- | text |
-- | txt |
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337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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-- | rdacarrier |
347 ## - | |
-- | text file |
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-- | rda |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Production management. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer-aided engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational intelligence. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Industrial engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Production engineering. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Industrial and Production Engineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer-Aided Engineering (CAD, CAE) and Design. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Operations Management. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational Intelligence. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2190-5053 |
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-- | ZDB-2-ENG |
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