NDLI: Data-driven inline optimization of the manufacturing process of car body parts

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Data-driven inline optimization of the manufacturing process of car body parts

Content Provider	Scilit
Author	Purr, S. Wendt, A. Meinhardt, J. Moelzl, K. Werner, A. Hagenah, H. Merklein, M.
Copyright Year	2016
Description	Journal: Iop Conference Series: Materials Science and Engineering The manufacturing process of car body parts needs to be adaptable during production because of fluctuating variables; finding the most suitable settings is often expensive. The cause-effect relation between variables and process results is currently unknown; thus, any measure taken to adjust the process is necessarily subjective and dependent on operator experience. To investigate the correlations involved, a data mining system that can detect influences and determine the quality of resulting parts is integrated into the series process. The collected data is used to analyze causes, predict defects, and optimize the overall process. In this paper, a data-driven method is proposed for the inline optimization of the manufacturing process of car body parts. The calculation of suitable settings to produce good parts is based on measurements of influencing variables, such as the characteristics of blanks. First, the available data are presented, and in the event of quality issues, current procedures are investigated. Thereafter, data mining techniques are applied to identify models that link occurring fluctuations and appropriate measures to adapt the process so that it addresses such fluctuations. Consequently, a method is derived for providing objective information on appropriate process parameters.
Related Links	http://iopscience.iop.org/article/10.1088/1757-899X/159/1/012002/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/159/1/012002
Journal	Iop Conference Series: Materials Science and Engineering
Volume Number	159
Language	English
Publisher	IOP Publishing
Publisher Date	2016-11-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Automotive Engineering Data Mining Manufacturing Process Mining System Inline Optimization Process Parameters Car Body Parts Process of Car
Content Type	Text
Resource Type	Article

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