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001 9910483272903321
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010   $a3-030-03201-9
024 7 $a10.1007/978-3-030-03201-2
035   $a(CKB)4100000007204986
035   $a(MiAaPQ)EBC5613410
035   $a(DE-He213)978-3-030-03201-2
035   $a(PPN)243770855
035   $a(EXLCZ)994100000007204986
100   $a20181211d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSoft Modeling in Industrial Manufacturing /$fedited by Przemyslaw Grzegorzewski, Andrzej Kochanski, Janusz Kacprzyk
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (200 pages)
225 1 $aStudies in Systems, Decision and Control,$x2198-4182 ;$v183
311   $a3-030-03200-0 
320   $aIncludes bibliographical references and index.
327   $aData and modeling in industrial manufacturing -- From data to reasoning -- Data preprocessing in industrial manufacturing -- Tool condition monitoring in metal cutting -- Assessment of selected tools used for knowledge extraction in industrial manufacturing -- Application of data mining tools in shrink sleeve labels converting process -- Study of thickness variability of the ?oorboard surface layer -- Applying statistical methods with imprecise data to quality control in cheese manufacturing -- Monitoring series of dependent observations using the sXWAM control chart for residuals -- Diagnosis of out-of-control signals in complex manufacturing processes.
330   $aThis book discusses the problems of complexity in industrial data, including the problems of data sources, causes and types of data uncertainty, and methods of data preparation for further reasoning in engineering practice. Each data source has its own specificity, and a characteristic property of industrial data is its high degree of uncertainty. The book also explores a wide spectrum of soft modeling methods with illustrations pertaining to specific cases from diverse industrial processes. In soft modeling the physical nature of phenomena may not be known and may not be taken into consideration. Soft models usually employ simplified mathematical equations derived directly from the data obtained as observations or measurements of the given system. Although soft models may not explain the nature of the phenomenon or system under study, they usually point to its significant features or properties.
410  0$aStudies in Systems, Decision and Control,$x2198-4182 ;$v183
606   $aComputational intelligence
606   $aEngineering mathematics
606   $aIndustrial engineering
606   $aProduction engineering
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aEngineering Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/T11030
606   $aIndustrial and Production Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T22008
615  0$aComputational intelligence.
615  0$aEngineering mathematics.
615  0$aIndustrial engineering.
615  0$aProduction engineering.
615 14$aComputational Intelligence.
615 24$aEngineering Mathematics.
615 24$aIndustrial and Production Engineering.
676   $a658.8
702   $aGrzegorzewski$b Przemyslaw$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aKochanski$b Andrzej$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aKacprzyk$b Janusz$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910483272903321
996   $aSoft Modeling in Industrial Manufacturing$92843833
997   $aUNINA