Welding Process Quality Improvement with Machine Learning Techniques

This paper presents a methodology for selecting design parameters with influence in quality product, using data analysis and machine learning techniques applied to a manufacturing problem where execution results can provide data for design improvement. Techniques are developed and used to extract the maximum information from existing process and quality data, and therefore to get feedback at design stages regarding how parameters variations affect the product performance when certain quality tests are applied. An algorithm is developed to sample multidimensional points in order to get the most representative subset, and various techniques are applied for data cleaning and outliers detection, such as Mahalanobis distance, Cook distance and HDOutliers method. Finally, inductive learning techniques, such as decision trees and association rules, are implemented to get conclusions about the relationships of the different variables. This paper finally shows this methodology in an automotive welding scenario. Copyright (C) 2021 The Authors.