Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques

Abstract

Laser welding is particularly relevant in the industry thanks to its simplicity, flexibility and final quality. The industry 4.0 and sustainable manufacturing framework gives massive attention to in situ and non‐destructive inspection methods to predict laser weld final quality. Literature often resorts to supervised Machine Learning approaches. However, selecting the ApTest method is non‐trivial and often decision making relies on diverse and unclearly defined criteria. This work addresses this task by proposing a statistical comparison method based on nonparametric tests. The method is applied to the most relevant supervised Machine Learning approaches exploited in literature to predict laser weld quality, specifically, considering the optimisation of a new production line, hence focussing on supervised Machine Learning methods that do not require massive data set, that is, Generalized Linear Model (GLM), Gaussian Process Regression, Support Vector Machine, Classification and Regression Tree, and Genetic Algorithms. The statistical comparison is carried out to select the best‐performing model, which is then exploited to optimise the production process. Additionally, an automatic process to optimise Machine Learning models and process parameters is resorted to, basing on Bayesian approaches, to reduce operator effect. This work provides quality and process engineers with a simple framework to compare Machine Learning approaches performances and select the most suitable process modelling technique.

Quantitative analysis of predictors of acoustic materials for noise reduction as sustainable strategies for materials in the automotive industry

“…This scrap comprises areas of the blank that cannot be used due to shape mismatch with strip dimensions, etc., and the results of unproductive press blows [20]. This has become an important factor in the choice of materials in vehicle construction along with costeffective, ergonomic [21], and environmental impact considerations [22].…”

The ENBIS‐22 quality and reliability engineering international special issue

“…Rizzo and Bucchianico 2 propose a control chart based on a generalized linear model for the case where the characteristic to be tracked depends on covariates.The following five articles deal directly with machine learning: the first three in an industrial context, the others for variable selection with a view to explicability. Maculotti et al 3 objective is to compare different machine learning approaches with the aim of selecting the best one to predict the final quality of laser welds which allows to stick to non-destructive inspection methods as recommended by Industry 4.0. Cacciarelli et al 4 pose the original question of online active learning (learning algorithms that can actively query the user to obtain a label) in outliers-contaminated data streams; the method consists of both constraining the area of new labels and proposing a robust estimator.…”