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

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.

Publication DOI: https://doi.org/10.3390/su152015051
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Aston University (General)
Additional Information: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Funding: The research was funded by the National Science Foundation of China (#52275482) and the National Key R&D Program of China (#2022ZD0115404).
Uncontrolled Keywords: welding robots; process parameter optimization; multiple objectives; small sample size dataset; auto machine learning
Publication ISSN: 2071-1050
Last Modified: 05 Dec 2024 08:22
Date Deposited: 30 Oct 2023 15:11
Full Text Link:
Related URLs: https://www.mdp ... 050/15/20/15051 (Publisher URL)
PURE Output Type: Article
Published Date: 2023-10-19
Published Online Date: 2023-10-19
Accepted Date: 2023-10-13
Authors: Yan, Jihong
Zhang, Mingyang
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)

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