The product’s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvement of enterprise in the future.

中文翻译：

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考虑产品制造过程关键质量特性的关键程序识别方法

产品的制造工艺对产品质量有着明显的影响。为了合理有效地控制产品制造过程的质量和识别关键工序，提出了一种将遗传反向传播（BP）神经网络算法与灰色关联分析相结合的方法。首先，利用遗传BP神经网络算法获取产品制造过程中的关键质量特征（KQCs）。同时，考虑到对流程质量有本质影响的三个因素，采用灰色关联分析方法建立流程与KQC之间的相关评分矩阵，计算各流程的临界度。最后，以蒸发器的制造工艺为例，介绍了该方法的应用过程，确定了四个关键程序。为今后企业的流程质量控制和改进提供参考。