Abstract
The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network (BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases.
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Foundation item: the National Natural Science Foundation of China (Nos. 51405299 and 51175340), and the Natural Science Foundation of Shanghai (No. 14ZR1428700)
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Liu, Y., Ye, X. & Jin, S. A bayesian based process monitoring and fixture fault diagnosis approach in the auto body assembly process. J. Shanghai Jiaotong Univ. (Sci.) 21, 164–172 (2016). https://doi.org/10.1007/s12204-016-1708-1
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DOI: https://doi.org/10.1007/s12204-016-1708-1