Artificial Intelligence Applications for Friction Stir Welding: A Review

Artificial Intelligence Applications for Friction Stir Welding: A Review

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

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