论文标题
汽车行业中电阻点焊接过程数据的功能聚类方法
Functional clustering methods for resistance spot welding process data in the automotive industry
论文作者
论文摘要
汽车行业中金属板的电阻点焊接（RSW）关节的质量评估通常是基于昂贵且冗长的离线测试，这些测试在完整的生产中是不可行的，尤其是在大规模上。但是，由行业4.0框架触发的大规模工业数字化使每个生产的联合，在线RSW过程参数（尤其是所谓的动态阻力曲线（DRC））都可以被视为现场焊缝的完整技术签名。在这种情况下，本文意味着显示功能数据方法的聚类方法的潜力和实际适用性，这些方法避免了需要任意且经常有争议的特征提取，以发现DRC的均匀群体，这些drc可能与将共享的常见机械和金属性特性相关。我们打算为最有希望的功能聚类方法提供基本的实践概述，并将后者应用于手头RSW过程中收集的DRC，即使它们可以远远超出了所研究的特定应用程序。所分析的方法被证明可以在识别过程参数与RSW接头之间的最终质量之间的映射关系以及更具体地说，更具体地说，沿焊接点的离线测试和焊接工具磨损分析的优先级分配。通过软件环境R开发的分析代码，DRC数据集可在https://github.com/unina-sfere/funclustrsw/上公开提供。
Quality assessment of resistance spot welding (RSW) joints of metal sheets in the automotive industry is typically based on costly and lengthy off-line tests that are unfeasible on the full production, especially on large scale. However, the massive industrial digitalization triggered by the industry 4.0 framework makes available, for every produced joint, on-line RSW process parameters, such as, in particular, the so-called dynamic resistance curve (DRC), which is recognized as the full technological signature of the spot welds. Motivated by this context, the present paper means to show the potentiality and the practical applicability to clustering methods of the functional data approach that avoids the need for arbitrary and often controversial feature extraction to find out homogeneous groups of DRCs, which likely pertain to spot welds sharing common mechanical and metallurgical properties. We intend is to provide an essential hands-on overview of the most promising functional clustering methods, and to apply the latter to the DRCs collected from the RSW process at hand, even if they could go far beyond the specific application hereby investigated. The methods analyzed are demonstrated to possibly support practitioners along the identification of the mapping relationship between process parameters and the final quality of RSW joints as well as, more specifically, along the priority assignment for off-line testing of welded spots and the welding tool wear analysis. The analysis code, that has been developed through the software environment R, and the DRC data set are made openly available online at https://github.com/unina-sfere/funclustRSW/
