Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

Jihong Yan (), Mingyang Zhang and Yuchun Xu
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Jihong Yan: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Mingyang Zhang: School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Yuchun Xu: College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK

Sustainability, 2023, vol. 15, issue 20, 1-16

Abstract: The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability.

Keywords: welding robots; process parameter optimization; multiple objectives; small sample size dataset; auto machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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