A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

IF 3.3 Q2 ENGINEERING, MANUFACTURING Journal of Manufacturing and Materials Processing Pub Date : 2023-10-07 DOI:10.3390/jmmp7050181
D. A. P. Prabhakar, Akash Korgal, Arun Kumar Shettigar, Mervin A. Herbert, Manjunath Patel Gowdru Chandrashekharappa, Danil Yurievich Pimenov, Khaled Giasin
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Abstract

This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed.
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搅拌摩擦焊(FSW)工艺优化与测试技术综述
本文综述了搅拌摩擦焊工艺分析和工艺优化所采用的工具和工艺参数对连接件质量的影响。转速、倾角、横移速度、轴向力和刀具轮廓几何是影响接头质量的重要参数。根据不同的加工材料收集相应的数据,并采用不同的实验技术对其加工结果进行分析。分析了各种优化技术,指出了它们的潜在优势和局限性。过程测量技术可以在过程中使用传感器(力、扭矩、功率和温度数据)与FSW机器集成,从而实现反馈收集。讨论了信号处理与人工智能和机器学习算法相结合的应用,从而提高了焊接质量。
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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来源期刊
Journal of Manufacturing and Materials Processing
Journal of Manufacturing and Materials Processing Engineering-Industrial and Manufacturing Engineering
CiteScore
5.10
自引率
6.20%
发文量
129
审稿时长
11 weeks
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