Framework for Mitigation of Welding Induced Distortion through Response Surface Method and Reinforcement Learning

Waheed, Rehan; Saeed, Hasan Aftab; Butt, Sajid Ullah; Anjum, Bilal

doi:10.3390/coatings11101227

Cited by 3 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This mathematical relation was used by them for further optimization of the welding process through the genetic algorithm (GA). Waheed et al [8] used the response surface method and artificial intelligence to optimize welding induced distortion. Gunaraj and Murugan [9] used the response surface method for the optimization of weld process parameters for submerged arc welding.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Welding-Parameter Optimization Using Overlaid Contour Plots and the Butterfly Optimization Algorithm

2022

Self Cite

View full text Add to dashboard Cite

Distortion and residual stress are two unwelcome byproducts of welding. The former diminishes the dimensional accuracy while the latter unfavorably affects the fatigue resistance of the components being joined. The present study is a multi-objective optimization aimed at minimizing both the welding-induced residual stress as well as distortion. Current, voltage, and welding speed were the welding parameters selected. It was observed that the parameters that minimize distortion were substantially different from those that minimized the residual stress. That is, enhancing dimensional accuracy by minimizing distortion results in an intensification of residual stresses. A compromise between the two objectives was therefore necessary. The contour plots produced from the response surfaces of the two objectives were overlaid to find a region with feasible parameters for both. This feasible region was used as the domain wherein to apply the novel butterfly optimization algorithm (BOA). This is the first instance of the application of the BOA to a multi-objective welding problem. Weld simulation and a confirmatory experiment based on the optimum weld parameters thus obtained corroborate the efficacy of the framework.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Welding-Parameter Optimization Using Overlaid Contour Plots and the Butterfly Optimization Algorithm

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The validity of the model was tested by analysis of variance (ANOVA). Waheed et al [ 18 ] optimized the welding process parameters by RSM. Using optimal welding parameters optimizes the welding sequence.…”

Section: Introductionmentioning

confidence: 99%

Process Optimization, Microstructure and Mechanical Properties of Wire Arc Additive Manufacturing of Aluminum Alloy by Using DP-GMAW Based on Response Surface Method

Du,

Sun,

et al. 2023

Materials

View full text Add to dashboard Cite

Double-pulsed gas metal arc welding (DP-GMAW) is a high-performance welding method with low porosity and high frequency. Periodic shrinkage and expansion of the melt pool during DP-GMAW leads to unusual remelting, and the re-solidification behavior of the weld metal can significantly refine the weld structure. The advantages of DP-GMAW have been proven. In order to better apply DP-GMAW to aluminum alloy arc additive manufacturing, in this paper, the single-pass deposition layer parameters (double-pulse amplitude, double-pulse frequency and travel speed) of DP-GMAW will be optimized using the response surface method (RSM) with the width, height, and penetration of the deposition layer as the response values to find the superior process parameters applicable to the additive manufacturing of aluminum alloy DP-GMAW. The results show that the aluminum alloy components made by DP-GMAW additive are well formed. Due to the stirring of double-pulse arc and the abnormal remelting and solidification of metal, the microstructures in the middle and top areas show disordered growth. The average ultimate tensile strength of the transverse tensile specimen of the member can reach 175.2 MPa, and the elongation is 10.355%.

show abstract

“…Because of the complexity of the design space, the manual selection and verification method based on the experience of personnel is time-consuming and laborious, and the accuracy and the optimization effect of the method are insufficient. In recent decades, the response surface method (RSM), [11][12][13] hierarchical kriging (HK) method, [14][15][16][17] and radial basis function (RBF) method [18][19][20][21] have been proposed. These methods construct a proxy model between design variables and responses 22 for predicting the response value and avoiding the verification of data one by one.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective structural optimization of the aluminum alloy subway car body based on an approximate proxy model

Zhang

2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

To improve the comprehensive structural performance and optimize the structural quality of the aluminum alloy subway car body, a multi-objective structural optimization method based on a response surface approximate proxy model was proposed. On the basis of the sensitivity calculation of the objective response to the thickness changes of profiles in different regions of the car body, 21 groups of plate variables with the highest optimization potential were selected for optimization. The optimization experiments were performed on the design variables, and sufficient sample points were selected for the target analysis. Based on the calculated data, second-order response surface models for the bending modal frequency, bending stiffness, and complete mass of the subway car body were constructed considering the optimized profile thickness. After verifying the accuracy of the model, it was used to replace the finite element model for optimization analysis. To achieve feasible optimization results, two schemes with the profile thickness varied in steps of 0.1 and 0.5 mm in the design space were used. After the completion of the calculation, experimental evaluation of the two optimal schemes indicated that the structural performance was consistent with the numerical results.

show abstract

Framework for Mitigation of Welding Induced Distortion through Response Surface Method and Reinforcement Learning

Cited by 3 publications

References 21 publications

Multi-Objective Welding-Parameter Optimization Using Overlaid Contour Plots and the Butterfly Optimization Algorithm

Multi-Objective Welding-Parameter Optimization Using Overlaid Contour Plots and the Butterfly Optimization Algorithm

Process Optimization, Microstructure and Mechanical Properties of Wire Arc Additive Manufacturing of Aluminum Alloy by Using DP-GMAW Based on Response Surface Method

Multi-objective structural optimization of the aluminum alloy subway car body based on an approximate proxy model

Contact Info

Product

Resources

About