A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

Abstract

Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

“…Machine learning will combine the governing equations with plasticity models and experimental data. AI methods should also be implemented in novel superhard materials for high-performance [87,88,94,[95][96][97][98][99][100][101][102][103][104][105][106][107].…”

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

“…[ 23,131–134 ] After the establishment of the database and the analysis of the IFs, different data‐driven algorithms were used to predict the fatigue performance. Specifically, several effectively approaches (Bayesian model, [ 135–137 ] ANN, [ 67,129,138–141 ] particle swarm optimization (PSO)‐BPNN, [ 75,142 ] Ant colony optimization‐BPNN, [ 39,55 ] SVM, [ 56,143–145 ] GA‐ANN, [ 146 ] GA‐BPNN, [ 146,147 ] RF, [ 89,137,148,149 ] DNN, [ 112,150–152 ] convolution neural network (CNN), [ 153–155 ] long short‐term memory (LSTM), [ 156–158 ] radial basis function neural network (RBFNN), [ 53,88,159,160 ] etc.) were developed to realize the fatigue performance prediction of welded joints.…”

“…Correlation between multiple IFs and fatigue life. [90] www.advancedsciencenews.com www.aem-journal.com SVM, [56,[143][144][145] GA-ANN, [146] GA-BPNN, [146,147] RF, [89,137,148,149] DNN, [112,[150][151][152] convolution neural network (CNN), [153][154][155] long short-term memory (LSTM), [156][157][158] radial basis function neural network (RBFNN), [53,88,159,160] etc.) were developed to realize the fatigue performance prediction of welded joints.…”

Investigations on the effect of dielectric medium and WEDM parameters on surface characteristics of Al 7068 (ordnance aluminium) alloy

“…To estimate the error % of any process, ML techniques including the adaptive neuro fuzzy interference system, regression model, support vector machine, and artificial neural networks can be adopted. [40,41]. Artificial neural networks (ANNs) are used as a powerful tool for forecasting surface roughness (SR) and material removal rate (MRR) in any machining process [42].…”