Development of an optimal process for friction stir welding based on GA-RSM hybrid algorithm

Khansari, Nabi Mehri; Berto, Filippo; Karimi, Namdar; Ghoreishi, Seyed Mohammad Navid; Fakoor, Mahdi; Mokari, M.E.

doi:10.3221/igf-esis.44.09

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Tool rotation speed, welding/traverse speed, tool inclination angle, axial force, and plunge depth are the primary variables. Furthermore, it has been reported that the rotational speed of the tool along with traverse speed in the second position is the most significant parameter for impact strength [6], elongation percentage [7], and other mechanical properties of the weld [8]. This was positively verified by [9] through experimental and numerical investigation for lap joints of high-density polyethylene (HDPE).…”

Section: Introductionmentioning

confidence: 90%

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

Mishra

Potnis

Sapre

et al. 2023

Mater. Res. Express

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The friction stir spot welding (FSSW) process is a novel technique that overcomes the limitation of resistance spot welding. Recently, FSSW used for welding of polymers which are difficult to be joined by traditional welding processes. The demand for Acrylonitrile Butadiene Styrene (ABS) for industrial applications has increased in recent years. However, to employ this technique the challenge is to get optimal FSSW parameters setting to achieve the best weld strength during the welding of ABS sheets. To achieve this, in the present work, full factorial experimental design layout was employed to investigate the effect of process parameters on weld strength i.e., ultimate tensile strength (UTS) and percentage elongation during FSSW of ABS-ABS sheet in butt configuration. To predict the UTS and percentage elongation, machine learning regression namely, linear, polynomial, support vector machine, and decision tree was used. Further, the study includes the identification of the fracture patterns post tensile test specimens based on the topography of the fracture surface under scanning electron microscopy. It was found that plunge depth is the most significant parameter followed by spindle speed and dwell time. The optimal setting of process parameters i.e., spindle speed of 1000 rpm, plunge depth of 1 mm, and dwell time of 40 seconds resulted in maximum UTS of 7.849 MPa. The maximum value of percentage elongation obtained was 5 at the parameter setting of spindle speed of 1000 rpm, plunge depth of 0.8 mm, and dwell time of 40 seconds. Polynomial regression outperformed in the prediction of UTS and percentage elongation with an R-square of 0.99.

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Section: Introductionmentioning

confidence: 90%

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

Mishra

Potnis

Sapre

et al. 2023

Mater. Res. Express

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“…The model was able to predict the ultimate tensile strength of FSW joints within the 95% confidence level. Khansare et al [17] proposed a hybrid optimization methodology based on the combination of the RSM and a genetic algorithm (GA) [18] to approximate the optimal welding speed and tool rotational speed in which a maximum ultimate tensile strength could be achieved.…”

Section: State Of the Art-use Of Optimization Algorithms In The Fieldmentioning

confidence: 99%

Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization

Hartl

Hansjakob

Zaeh

2020

Int J Adv Manuf Technol

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Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown a significant dependence of the welding quality on the welding speed and the rotational speed of the tool. Frequently, an inappropriate setting of these parameters can be detected through an examination of the resulting surface defects, such as increased flash formation or surface galling. In this work, two different learning-based algorithms were applied to improve the surface topography of friction stir welds. For this purpose, the surface topographies of 262 welds, which were performed as part of ten studies, were evaluated offline. The aim was to use reinforcement learning and Bayesian optimization approaches to determine the most appropriate settings for the welding speed and the rotational speed of the tool. The optimization problem was solved using reinforcement learning, specifically value iteration. However, the value iteration algorithm was not efficient, since all actions and states had to be iterated over, i.e., each possible parameter combination had to be evaluated, to find the best policy. Instead, it was better to solve the optimization problem directly using the Bayesian optimization. Two approaches were applied: both an approach in which the information from the other studies was not used and an approach in which the information from the other studies was used. On average, both the Bayesian optimization approaches found suitable welding parameters significantly faster than a random search algorithm, and the latter approach improved the result even further compared with the former approach. Future research will aim to show that optimization of the surface topography also leads to an increase in the ultimate tensile strength.

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“…Few researchers have used the optimization technique in the friction stir welding field but very little in case of UWFSW. The ANFIS Integrated Hybrid Methods are provided in this study for successfully modeling the UWFSW parameters with performance reaction values [20][21][22]. The RSM-GA Integrated Hybrid Methods are suggested in this research for modeling the UWFSW parameters.…”

Section: Introductionmentioning

confidence: 99%

Implementation of hybrid RSM-GA optimization techniques in underwater friction stir welding

Sabry

El-Zathry

Gadallah

et al. 2022

J. Phys.: Conf. Ser.

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Standard friction stir welding process parameters have a considerable impact on the quality of functional parts produced by underwater friction stir welding (UWFSW) with additive water. Hybrid statistical techniques may be used to optimize operating parameters in order to improve the aim function. The tensile strength (UTS)of parts fabricated with UWFSW by Al 6063 material in accordance with ASTMD638-14 tests is investigated in this study. In the construction of test specimens, three parameters were varied: rotational speed from 1000 to 1800 rpm, travel speed from 4 to 10 mm/s, and shoulder diameter from 10 to 20 mm. The response surface methodology (RSM) based central composite design (CCD) matrix for the parametric combination was constructed using a second-order polynomial fitting model. The maximum UTS of testing samples on the 201T universal testing machine (UTM) was 208.27 MPa. These process parameters are also optimized using hybrid optimization approaches such as response surface methodology- genetic algorithm (RSM-GA). RSM-GA had the highest precision of 98.99 percent, which resulted in optimal characteristics such as rotating speed 1800 rpm, travelling speed 4 mm/s, and shoulder diameter 15 mm, which resulted in a maximum tensile strength of 199.0212 MPa.

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Development of an optimal process for friction stir welding based on GA-RSM hybrid algorithm

Cited by 4 publications

References 26 publications

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization

Implementation of hybrid RSM-GA optimization techniques in underwater friction stir welding

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