汽车冲压“工艺-质量”商业智能分析

Business intelligence of automobile“process-quality”stamping production

为适应汽车冲压生产智能制造的要求,针对车间生产质量控制严重依赖人工经验,生产数据关系难以建立等问题,本文利用偏最小二乘回归方法(PLS)建立映射关系,最终提出冲压商业智能分析模型,用于深入挖掘冲压“工艺—质量”关联关系,并确定影响冲压生产质量关键因子。本模型通过采集冲压工艺过程数据和零件质量数据,采用主成分分析方法对多组自变量进行分类和降维,提取4组主成分作为解释源数据的综合性指标,并结合回归模型中的标准回归系数确定了9个关键因子。经检验,模型判定系数R^(2)=0.7,模型具有简单可靠的预测效果。在实际应用中,该模型可有效建立不同类别零件一对多甚至多对多的映射关系,实现质量超前控制,提高零件下线质量的一致性和稳定性。

To reach the goal of advanced quality control of automobile stamping production,aiming at the problem that production quality relied heavily on manual experience and production data relationship was hard to establish,the Partial Least Square regression(PLS)method was used to constructed a mapping relationship,and a stamping Business Intelligence(BI)analysis model was established to further explore the“process-quality”relationship and determine the key factors affecting the stamping production quality.The multiple groups of independent variables were classified and dimensionality reduced with Principal Component Analysis(PCA)by collecting stamping process data and parts quality data.Four groups of principal components were extracted as comprehensive indicators to interpret the source data,and nine key factors were determined based on the standard regression coefficients in the regression model.Upon examination,model determination coefficient was equal to 0.8,which could prove a practical and reliable prediction result.In practical application,the model could effectively establish one-to-many or even many-to-many mapping relationship of different parts,which improved the consistency and stability of parts'quality and achieved advanced control of production quality.