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

Abstract

By controlling various friction stir spot-welded (FSSW) factors, two base sheets AA 5052-H32 and 6061-T6 were selected to bond similar and dissimilar metal joints while considering dissimilar configuration orders. The effects of weld parameters on the sheer strength and peel strength were separately developed into empirical models utilizing the integrated central composite matrix design and response surface methodology (RSM). Meanwhile, the finite element (FE) analysis of the multi-axis load-bearing characteristics for automotive solder joints during service was carried out. As a result, the weights of the shear and axial stress, accounting for 90.5% and 9.5% respectively, were employed to restrict the relationship between multiple target properties, and the resulting security strength was applied to determine the feasible domain in subsequent parametric optimization. In order to enable the optimal multi-axis capacities in accordance with the load mode, the genetic algorithm NSGA-II was chosen to compute the Pareto front and further determine the best compromise solutions. The obtained optimums corresponding to each joining condition were validated by confirmation runs, indicating that this coupled multi-objective optimization approach based on working conditions was beneficial to the targeted improvement of post-weld mechanical properties.

Fractal Dimension Characterization of Joint Surface Morphology on Dissimilar Friction Stir Lap Welding of Al/Mg

“…It allows the statistical analysis of different factors and variables. Based on the central composite design (CCD) [18], with the rotational and welding speeds as two dependent variables and the fractal dimension D as the response surface value, a quadratic polynomial was used to fit the functional relationship between the designed parameters and the response surface value. The least squares method was used to obtain the undetermined coefficients of each item, after which the function model of the response surface was fitted.…”

Process parameter optimisation in pinless friction stir spot welding of dissimilar aluminium alloys using Multi-start algorithm

“…14–18 Hybrid optimisation techniques like the Taguchi method integrated with grey relational analysis and principal component analysis were utilised in the multi-objective study in FSSW of Al 2219-O. 19,20 Recently advanced modelling techniques like artificial neural network (ANN) with metaheuristic algorithms like genetic algorithm (GA) and particle swarm algorithm (PSO) have been employed to predict the mechanical properties and optimise the process parameters in friction stir welding (FSW) of aluminium joints. 21–23…”

Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

“…Through the multi-objective optimization of the performance and deformation degree of the diffusion bonding process, several sets of parameters that meet the strength requirements and do not cause an excessive distortion of parts can be found. These parameters are called Pareto solutions in multi-objective optimization problems [10][11][12]. Obviously, in order to realize the multi-objective optimization of the diffusion bonding process, it is necessary to establish a fast response model between the bonding parameters (bonding temperature, bonding pressure, duration) and diffusion bonding performance and deformation ratio.…”