力和扭矩在搅拌摩擦焊接中的作用:详细综述

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mostafa Akbari , Milad Esfandiar , Amin Abdollahzadeh
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引用次数: 0

摘要

搅拌摩擦焊(FSW)是一种重要的金属和聚合物固态连接技术,有效地解决了熔焊带来的挑战。FSW的应用依赖于开发具有成本效益,耐用的工具,以始终如一地产生高质量的焊缝。焊接过程中产生的力和扭矩对该过程至关重要,影响焊接完整性、工艺效率和工具寿命。这篇综述探讨了估计这些参数的方法-分析,数值和实验-并讨论了测量技术,包括直接和间接方法。它还研究了不同FSW类型(如传统FSW、Bobbin Tool FSW和Stationary Shoulder FSW)的受力变化,强调了它们在操作机制上的差异。此外,该综述还强调了如何优化工具形状、尺寸、倾斜角和焊接速度等工艺参数以提高性能,并研究了力测量在实时焊缝监测和缺陷检测中的应用,从而提高了FSW在工业应用中的可靠性。结果表明,利用力测量对焊接过程进行在线监测,特别是对焊接缺陷和整体焊接质量的监测,近年来受到了广泛关注。该领域的一个显著进步是机器学习工具的实施,它增强了预测潜在焊接缺陷和提高整体焊接质量的能力。这种创新的方法不仅简化了监测过程,而且有助于FSW技术的发展,确保在各种应用中达到更高的质量和安全标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The role of force and torque in friction stir welding: A detailed review
Friction Stir Welding (FSW) is a significant solid-state joining technique for metals and polymers, effectively addressing challenges posed by fusion welding. The application of FSW relies on the development of cost-effective, durable tools that consistently produce high-quality welds. The forces and torque generated during welding are critical to this process, which influence weld integrity, process efficiency, and tool longevity. This review explores methodologies for estimating these parameters—analytical, numerical, and experimental—and discusses measurement techniques, including direct and indirect methods. It also examines variations in forces across different FSW types, such as Conventional FSW, Bobbin Tool FSW, and Stationary Shoulder FSW, emphasizing the differences in their operational mechanics. Additionally, the review highlights how process parameters like tool shape, size, tilt angle, and welding speed can be optimized to enhance performance and investigates the use of force measurements for real-time weld monitoring and defect detection, contributing to the reliability of FSW in industrial applications. The results indicate that the use of force measurement for online monitoring of welding processes, particularly concerning welding defects and overall weld quality, has garnered significant attention in recent years. ​ A notable advancement in this field is the implementation of machine learning tools, which enhance the ability to predict potential weld defects and improve overall weld quality. This innovative approach not only streamlines the monitoring process but also contributes to the evolution of FSW technologies, ensuring higher standards of quality and safety in various applications.
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来源期刊
CiteScore
7.10
自引率
9.80%
发文量
58
审稿时长
44 days
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