Abstract

Advances in artificial intelligence (AI) techniques that can be used for different purposes have enabled it to be used in many different industrial applications. These are mainly used for modeling, identification, optimization, prediction and control of complex systems under the influence of more than one parameter in industrial applications. With the increasing accuracy of AI techniques, it has also obtained a wide application area on friction stir welding (FSW), one of the production methods developed in recent years. In this study, commonly used AI techniques for FSW, results, accuracy and superiority of AI techniques are reviewed and evaluated. In addition, an overview of AI techniques for FSW in different material combinations is provided. Considering the articles examined; It is seen that welding speed, rotational speed, the plunge depth, spindle torque, shoulder design, base material, pin design/profile, tool type are used as input parameters and tensile strength, yield strength, elongation, hardness, wear rate, welding quality, residual stress, fatigue strength are used as output parameters. As can be seen from the studies, it made important contributions in deciding what input parameters should be in order to have the output parameter at the desired value. The most common used materials for FSW are Al, Ti, Mg, Brass, Cu and so on. When FSW studies using artificial intelligence techniques were examined, it was seen that 81% of the most used materials were AL alloys and 23% of them were made with dissimilar materials. The most commonly utilized AI techniques were said to be artificial neural networks (ANN), fuzzy logic, machine learning, meta-heuristic methods and hybrid systems. As a result of the examination, ANN was the most widely used method among these methods. However, in recent years, with the exploration of new hybrid methods it was seen that hybrid systems used with ANN have higher accuracy.

Graphic Abstract

中文翻译：

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搅拌摩擦焊接的人工智能应用综述

摘要

可以用于不同目的的人工智能（AI）技术的进步使它可以在许多不同的工业应用中使用。这些主要用于在工业应用中受多个参数影响的复杂系统的建模，识别，优化，预测和控制。随着AI技术精度的提高，它在摩擦搅拌焊（FSW）上也获得了广阔的应用领域，这是近年来开发的一种生产方法。在这项研究中，对FSW的常用AI技术，AI技术的结果，准确性和优越性进行了审查和评估。另外，提供了在不同材料组合中用于FSW的AI技术的概述。考虑审查的条款；可以看出，焊接速度，转速，切入深度，主轴扭矩，肩部设计，基础材料，销设计/轮廓，工具类型用作输入参数，而抗拉强度，屈服强度，伸长率，硬度，磨损率，焊接质量，残余应力，疲劳强度用作输出参数。从研究中可以看出，它在决定什么输入参数以使输出参数达到所需值方面做出了重要贡献。FSW最常用的材料是Al，Ti，Mg，黄铜，Cu等。当检查使用人工智能技术的FSW研究时，可以发现81％的最常用材料是AL合金，而23％的材料是由异种材料制成的。据说最常用的AI技术是人工神经网络（ANN），模糊逻辑，机器学习，元启发式方法和混合系统。作为检查的结果，在这些方法中，ANN是使用最广泛的方法。然而，近年来，随着对新的混合方法的探索，可以看到与人工神经网络一起使用的混合系统具有更高的精度。

图形摘要