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

IDDRG2016 conference on "Challenges in Forming High-Strength Sheets".

Purr, S.^1 ; Wendt, A.^1 ; Meinhardt, J.^2 ; Moelzl, K.^1 ; Werner, A.^1 ; Hagenah, H.^3 ; Merklein, M.^3

BMW Plant Regensburg, Regensburg

93055, Germany^1

BMW Group, Munich

80788, Germany^2

Department of Manufacturing Technology, Friedrich-Alexander University, Erlangen

91058, Germany^3

关键词: Data mining system;    Data-driven methods;    Manufacturing process;    Measurements of;    Objective information;    Overall process;    Process parameters;    Quality issues;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/159/1/012002/pdf
DOI  :  10.1088/1757-899X/159/1/012002

来源: IOP

【 摘 要 】

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.