The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

A. Kubit, Grzegorz Kłosowski, Wojciech Berezowski
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Abstract

This paper presents a machine learning and image segmentation based advanced quality assessment technique for thin refill friction stir spot welded (RFSSW) joints. In particular, the research focuses on developing a predictive support vector machines (SVM) model. The purpose of this model is to facilitate the selection of RFSSW process parameters in order to increase the shear load capacity of joints. In addition, an improved weld quality assessment algorithm based on optical analysis was developed. The research methodology includes specimen preparation stages, mechanical tests, and algorithmic analysis, culminating in a machine learning model trained on experimental data. The results demonstrate the effectiveness of the model in selecting welding process parameters and assessing weld quality, offering significant improvements compared to standard techniques. The optimized SVM model, employing the radial basis function (RBF) kernel, achieved a lower root mean square error of 257.9 and a high correlation coefficient of 0.95, indicating a strong linear relationship between the predicted and actual shear load capacities. This research not only proposes a novel approach to optimizing welding parameters but also facilitates automatic quality assessment, potentially revolutionizing and spreading the application of the RFSSW technique in various industries.
利用人工智能评估填充式搅拌摩擦点焊薄接头的质量
本文介绍了一种基于机器学习和图像分割的薄填充搅拌摩擦点焊 (RFSSW) 接头先进质量评估技术。具体而言,研究重点是开发一个预测性支持向量机 (SVM) 模型。该模型旨在帮助选择 RFSSW 工艺参数,以提高接头的剪切承载能力。此外,还开发了一种基于光学分析的改进型焊接质量评估算法。研究方法包括试样制备阶段、机械测试和算法分析,最终形成一个根据实验数据训练的机器学习模型。结果表明,该模型在选择焊接工艺参数和评估焊接质量方面非常有效,与标准技术相比有显著改进。采用径向基函数 (RBF) 内核的优化 SVM 模型的均方根误差较低,为 257.9,相关系数高达 0.95,表明预测的剪切承载能力与实际的剪切承载能力之间具有很强的线性关系。这项研究不仅提出了优化焊接参数的新方法,还促进了自动质量评估,有可能彻底改变和推广 RFSSW 技术在各行各业的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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