Prediction and optimization of friction welding parameters for joining aluminium alloy and stainless steel

Paventhan, R.; Lakshminarayanan, P.R.; Balasubramanian, V.

doi:10.1016/s1003-6326(11)60884-4

Cited by 83 publications

(35 citation statements)

References 10 publications

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“…Canyurt [26] developed the genetic algorithm welding strength model to estimate the mechanical properties of the welded joints for the brass materials. Paventhan et al [27] used the RSM to optimise the friction welding parameters for joining aluminium alloy and stainless steel. Sathiya et al [28] have conducted the optimisation of friction welding parameters using simulated annealing (SA), artificial neural networks (ANN) and evolutionary algorithms (EA).…”

Section: Optimisation Of Welding Parametersmentioning

confidence: 99%

Effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints

Winiczenko

2015

Int J Adv Manuf Technol

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This paper presents an effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints. A hybrid response surface methodology (RSM) and genetic algorithm (GA)-based technique were successfully developed to model, simulate, and optimise the welding parameters. Direct and interaction effects of process parameters on the ultimate tensile strength (UTS) were studied by plotting graphs. Friction force and friction time have a positive effect on tensile strength. As friction force and friction time increase, the tensile strength also increases. The maximum tensile strength of the friction-welded low carbon steel-ductile iron joints was 87 % of that of the base metal. The tensile properties, microstructure, Vickers hardness distribution, and fracture morphology of the welded specimen have been studied and presented in this study. Additionally, the distribution of carbon element on both sides of the interface was estimated using energydispersive spectroscopy (EDS). The results of the metallographic study show clearly that the friction welding process was accompanied by a diffusion of carbon atoms from ductile iron to steel. This process causes the formation of a carbonrich zone at the interface and decarburization zone in the ductile iron close to the bond interface.

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Section: Optimisation Of Welding Parametersmentioning

confidence: 99%

Effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints

Winiczenko

2015

Int J Adv Manuf Technol

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“…ASTM: E8 guidelines are followed in preparing the test specimens. From the literature [13][14][15][16][17][18][19][20][21][22], among many independently controllable primary and secondary process parameters affecting the tensile strength, Vickers Hardness etc., the primary process parameters are considered in ratios (friction pressure/friction time (F), forging pressure/forging time(D), rotational speed/Sec (N)). This is experimented to make as three factors.…”

Section: Evaluations Of Parent Metal Properties and Finding The Workimentioning

confidence: 99%

Analysis of friction welding parameters on the mechanical metallurgical and chemical properties of AISI 1035 steel joints

Selvamani

Palanikumar

Umanath

et al. 2015

Materials & Design (1980-2015)

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“…Paventhan et al [16,17] used the RSM to optimize the friction welding parameters for joining aluminium alloy and stainless steel. Sathiya et al [18,19] have done the optimization of friction welding parameters using simulated annealing and evolutionary computational techniques.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of the process parameters for friction welding of dissimilar metals

et al. 2019

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This paper presents a multi-objective optimization method for optimizing the process parameters during friction welding of dissimilar metals. The proposed method combines the response surface methodology (RSM) with an a genetic algorithm (GA) method. Ultimate tensile strength (UTS), flash diameter and the heat affected zone (HAZ) width of friction welded nodular cast iron with low carbon steel joints were investigated considering the following process parameters: friction pressure (FP), friction time (FT) and upsetting pressure (UP). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. In the case of UTS, FT has high significance followed by: FP and UP. Friction time has high significance on the flash diameter of nodular cast iron followed by UP and FP. However in the case of the low carbon steel flash diameter, UP has high significance followed by FT and FP. In the case of the HAZ width for nodular cast iron and low carbon steel side, friction time has high significance followed by UP and FT. Multiobjective optimization for maximizing the tensile strength and minimizing the flash diameter and the HAZ width was carried out using mathematical model.

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Prediction and optimization of friction welding parameters for joining aluminium alloy and stainless steel

Cited by 83 publications

References 10 publications

Effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints

Effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints

Analysis of friction welding parameters on the mechanical metallurgical and chemical properties of AISI 1035 steel joints

Multi-objective optimization of the process parameters for friction welding of dissimilar metals

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