Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning (Record no. 52867)

000 -LEADER
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
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- 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
912 ## -
-- ZDB-2-ENG

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