Data-driven Weld Nugget Width Prediction with Decision Tree Algorithm
Data-driven Weld Nugget Width Prediction with Decision Tree Algorithm
复制标题
DOI：
10.1016/j.promfg.2017.07.092
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Fahim Ahmed;Kyoung-Yun Kim
中科院分区：
文献类型：
--
作者：
Fahim Ahmed;Kyoung-Yun Kim
本文提出了一种决策树算法的能力，实现数据驱动的电阻点焊（RSW）的可焊性预测。尽管RSW提供了值得称赞的优点，例如低成本和高速/大容量操作，但是RSW过程通常是不一致的，并且这些显著的不一致是众所周知的可靠性问题。点焊过程和数据的挑战，包括不一致性往往阻碍了利用数据驱动的可焊性预测。在本文中，我们应用决策树算法从汽车OEM收集的RSW数据集绘制回归树，并提取决策规则的焊缝熔核宽度预测。三个RSW测试数据集，我们得出结论，决策树有助于预测熔核宽度，并确定设计和工艺参数的影响，熔核宽度响应变量。
This paper presents the capability of a decision tree algorithm to realize a data-driven resistance spot welding (RSW) weldability prediction. Although RSW provides commendable advantages, such as low cost and high speed/high volume operations, the RSW processes are often inconsistent and these significant inconsistencies are a well-known reliability issue. RSW process and data challenges including inconsistency often hinder the utilization of the data-driven weldability prediction. In this paper, we apply a decision tree algorithm on the RSW dataset collected from an automotive OEM to plot regression trees and to extract decision rules for the weld nugget width prediction. With three RSW test datasets, we conclude that the decision trees help in predicting the nugget width and in determining the impact of design and process parameters to the nugget width response variable.