大数据分析在汽车电枢电阻焊参数自优化中的研究  

Research on Self-optimization of Automotive Armature Resistance Welding Parameters Based on Big Data Analysis

作　　者：张毕生 胡玉茹 ZHANG Bi-sheng;HU Yu-ru(Bosch Automotive Products (Changsha) Co.,Ltd.,Changsha 410100,China)

机构地区：[1]博世汽车部件(长沙)有限公司

出　　处：《机电工程技术》2019年第8期67-70,共4页Mechanical & Electrical Engineering Technology

摘　　要：电阻点焊是汽车及其零部件制造中最主要的焊接方法之一,焊接质量的好坏将直接影响焊接件的连接性以及产品性能,而焊接参数的稳定性是影响点焊质量的主要因素之一。针对某汽车零部件企业电子驱动电机转子单件流生产线整流子焊接过程中,缺少焊接质量过程控制以及无法自适应调整焊接参数的问题,结合现有的工艺条件,提出了一种基于大数据分析的MathEngine模型实现焊接参数自优化,可根据焊接工况变化动态调整焊接参数,提高焊接质量,保证焊接精度及稳定性,同时通过动态UIR曲线监控焊接过程稳定性以及通过焊接质量评价模型实现焊接质量的预测及趋势分析,消除焊接飞溅,降低使用成本,节能降耗。Resistance spot welding is one of the most important welding methods in the manufacture of automobiles and their parts. The quality of welding will directly affect the product performance,and the stability of welding parameters is the one of the main factors affecting the quality of spot welding. To solving the problem of lacking of welding quality process control and the inability to adaptively adjust the welding parameters,basing on the existing process conditions,the MathEngine model basing on big data analysis is proposed to realize self-optimization of welding parameters. The MathEngine model can dynamically adjust welding parameters according to welding conditions, improve welding quality, ensure welding precision and stability,monitor the stability of welding process through dynamic UIR curve and achieve trend analysis of welding quality through welding quality evaluation model,aim to eliminate welding spatter,reduce the cost of use,save energy and reduce consumption.

关 键 词：电阻焊 自优化 大数据分析 UIR 

分 类 号：TG44[金属学及工艺—焊接]