000			03163nam a22003855i 4500
999			_c103362 _d103362 _x1
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005			20230102113127.0
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007			cr nn 008mamaa
008			150418s2015 gw | s |||| 0|eng d
020			_a9783319176116
024	7		_a10.1007/978-3-319-17611-6 _2doi
040			_bspa _dES-MaUEC
050		4	_aTS183 _b2015 EB
100	1		_aWuest, Thorsten. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/314869412/
245	1	0	_aIdentifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning _cby Thorsten Wuest.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XVIII, 272 páginas 139 ilustraciones, 10 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5053
490	0		_aEngineering (Springer-11647)
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	3		_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.
988			_aEBSPRINGER_2018
650		7	_aProcesos de fabricación _2embne _9163432
776	0	8	_iEdición impresa: _z9783319176123
776	0	8	_iEdición impresa: _z9783319176109
776	0	8	_iEdición impresa: _z9783319386980
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-17611-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI