Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis

Abstract

Machine learning (ML) methods have received immense attention as potential models for modeling different manufacturing systems. This paper presents a comprehensive review on the applications of ML methods in friction stir welding (FSW) field. Five main topics have been discussed: prediction of the joint properties, integration between ML and finite element methods, real-time control of FSW process, tool failure diagnosis, and incorporation between metaheuristic optimization techniques and ML methods. The common used ML methods such as multi-linear regression, K-nearest neighbor, random forest algorithm, Gaussian process regression, artificial neural network, support vector machine, radial basis function neural network, fuzzy system, adaptive neuro-fuzzy inference system, and random vector functional link are explained. Then, different statistical measures used to evaluate the performance of ML methods are presented. Finally, the applications of ML methods in FSW field are discussed. Important conclusions are drawn and future prospects are suggested.