000 03472nam a22005295i 4500
001 978-3-319-17611-6
003 DE-He213
005 20200420221255.0
007 cr nn 008mamaa
008 150418s2015 gw | s |||| 0|eng d
020 _a9783319176116
_9978-3-319-17611-6
024 7 _a10.1007/978-3-319-17611-6
_2doi
050 4 _aT55.4-60.8
072 7 _aTGP
_2bicssc
072 7 _aTEC009060
_2bisacsh
082 0 4 _a670
_223
100 1 _aWuest, Thorsten.
_eauthor.
245 1 0 _aIdentifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
_h[electronic resource] /
_cby Thorsten Wuest.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXVIII, 272 p. 139 illus., 10 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
505 0 _aIntroduction -- 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 _aThe 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.
650 0 _aEngineering.
650 0 _aProduction management.
650 0 _aComputer-aided engineering.
650 0 _aComputational intelligence.
650 0 _aIndustrial engineering.
650 0 _aProduction engineering.
650 1 4 _aEngineering.
650 2 4 _aIndustrial and Production Engineering.
650 2 4 _aComputer-Aided Engineering (CAD, CAE) and Design.
650 2 4 _aOperations Management.
650 2 4 _aComputational Intelligence.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319176109
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-17611-6
912 _aZDB-2-ENG
942 _cEBK
999 _c52867
_d52867