Multi objective optimization of friction stir welding parameters using FEM and neural network

Abstract

In this study the inuence of rotational and traverse speed on the friction stir welding of AA5083 aluminum alloy has been investigated. For this purpose a thermo-mechanically coupled, 3D FEM analysis was used to study the effect of rotational and traverse speed on welding force, peak temperature and HAZ width. Then, an Articial Neural Network (ANN) model was employed to understand the correlation between the welding parameters (rotational and traverse speed) and peak temperature, HAZ width and welding force values in the weld zone. Performance of the ANN model was found excellent and the model can be used to predict peak temperature, HAZ width and welding force. Furthermore, in order to find optimum values of traverse and rotational speed, the multi-objective optimization was used to obtain the Pareto front. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was employed to obtain the best compromised solution.

The effect of reinforcement type on the microstructure, mechanical properties, and wear resistance of A356 matrix composites produced by FSP

“…The finer elements in the mean size of 0.5 mm were placed under the tool pin. More details on FEM model can be found in authors' pervious works [7,16,[30][31][32][33][34][35]. …”

Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys

“…Shojaeefard et al 18 developed an ANN model between FSW parameters (welding speed and tool rotational speed) and mechanical properties (ultimate TS and hardness of AA7075-AA5083 butt joint). ANN was also used by Shojaeefard et al 19,20 to model between welding parameters and response variables of the welded joints. Further ANN and cellular automata finite element (CAFE) was used by Patel et al 21 to predict the GS and yield strength of FS-welded joint.…”

“…It is found that AI tools can better predict the behaviour of FSW process. [14][15][16][17][18][19][20][21] Keeping this in view, a hybrid approach of ANN-GA has been suggested and applied for modelling and optimization of FS-welded joint of these dissimilar aluminium alloys. First of all, ANN has been applied for the modelling of TS, average microhardness (MH) at weld nugget zone and average GS at weld nugget zone with the help of well-designed experimentation results.…”

Multi-Objective Optimization of Friction Stir Spot-Welded Parameters on Aluminum Alloy Sheets Based on Automotive Joint Loads

“…Thereby, the genetic algorithm (GA) or particle swarm algorithm can be applied to search for the Pareto-optimal sets. Shojaeefard et al [18] used an ANN-GA integrated approach based on finite element method (FEM) to investigate the correlations between FSW parameters and multiple output responses for AA5083 butt joints, and obtained the optimal parameter sets. Similarly, an ANN coupled with the particle swarm algorithm was proposed to establish the relationship between process parameters and mechanical properties, and the FSW parameters of dissimilar aluminum alloys (AA7075/AA5083) were further optimized [19].…”

“…Similarly, an ANN coupled with the particle swarm algorithm was proposed to establish the relationship between process parameters and mechanical properties, and the FSW parameters of dissimilar aluminum alloys (AA7075/AA5083) were further optimized [19]. Regarding the selection of optimization techniquesin the above literature surveys [18,19], the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was employed to determine the best compromise solution.…”