Multi-objective optimization of gas metal arc welding parameters and sequences for low-carbon steel (Q345D) T-joints

Shao, Qian; Xu, Tao; Yoshino, Tatsuo; Song, Nan

doi:10.1016/s1006-706x(17)30082-1

Cited by 27 publications

(9 citation statements)

References 22 publications

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“…In their work, Balan et al [24] optimized the bead geometry using a simulated annealing technique. Shao et al [25] optimized the welding stress and deformation by varying the electric current, voltage, and the welding speed using the design of experiments (DOE), which is a strategy for modeling experiments using statistical and mathematical techniques [26]. Gomes et al [19] studied a FCAW process for stainless-steel claddings.…”

Section: Theoretical Background a Stainless-steel Claddingsmentioning

confidence: 99%

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

et al. 2020

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One of the main goals in flux-cored arc welding processes is the optimization of bead geometry, in which multiple geometric characteristics of the welding bead are important; therefore, multiobjective optimization programming is often applied. However, several optimization problems that use stochastic programming do not consider the impact of the correlation between the output variables on their probabilistic constraints. In this context, this paper aims to present a multiobjective optimization method based on multivariate stochastic programming. To demonstrate the applicability of the proposal, we conducted a design of experiments to optimize a flux-cored arc welding process for stainless-steel claddings. The weighting-sums method was applied to formulate the multiobjective optimization problem. It was possible to formulate a multivariate probability distribution for the penetration and dilution. In addition, a 95% probability to meet the predefined specification limits of the geometric characteristics was achieved. INDEX TERMS Bead geometry, bivariate normal distribution, flux-cored arc welding, multiobjective optimization programming, stainless steel claddings, stochastic programming.

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Section: Theoretical Background a Stainless-steel Claddingsmentioning

confidence: 99%

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

et al. 2020

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“…According to the statistical knowledge, 24 the DOE table is established. The FE numerical simulations are carried out to obtain the total deformation.…”

Section: Test Design Scheme and Surrogate Modelmentioning

confidence: 99%

Multi-objective path optimization for arc welding robot based on discrete DN multi-objective particle swarm optimization

Wang

Min

2019

International Journal of Advanced Robotic Systems

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Rational weld seams sequence is important for improving welding productivity and quality. Hence, intelligent path optimization strategy is introduced to obtain optimized weld seams sequence in this article. The path length and total welding deformation are considered for multi-objective path planning. First, the optimization problem description is presented. At the same time, the path length and the total welding deformation of some sequences are calculated. Then, improved multi-objective particle swarm optimization algorithm is studied. In addition, the agent model is obtained based on the sample data and experiment design. At last, the proposed algorithm is applied to optimize the welding path length and total welding deformation. The simulation results show the effectiveness of the optimization strategy.

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“…The input variables for Jaya were: design parameters, size of selected population, and the maximum generations with values of 5, 20, and 20, respectively. The working zones of input variables were V ¼ [35, 50], WFR ¼ [19,25], WS ¼ [6,10], NPD ¼ [20,30]…”

Section: Multiresponse Optimization Using Ga and Jaya Algorithmmentioning

confidence: 99%

“…Diverse researchers all over the world suggested and applied numerous optimization techniques to obtain optimal welding conditions. [19][20][21] Albeit each of the aforementioned methods has their own pros and cons, recently the algorithms-based optimization methods are studied extensively in assessing its viability in commercial applications. 22,23 Thus, for maximizing the mechanical properties of all weld metals, in present work GA and Jaya algorithm are applied to the WASAW process parameters.…”

Section: Introductionmentioning

confidence: 99%

Investigating the effect of electrode preheating in novel water-cooled advanced submerged arc welding process

Choudhary

Gupta

Kumar

2018

Proceedings of the IMechE

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In this paper, a novel developed water-cooled advanced submerged arc welding (WASAW) process has been designed and established. The developed WASAW process can be utilized for higher preheating currents at 100% duty cycle. Subsequently, a multi-pass welding investigation has been carried out on AISI 1023 steel plates as per the L8 Taguchi orthogonal array by varying WASAW input factors namely voltage (V), wire feed rate (WFR), welding speed (WS), nozzle to plate distance (NPD), and preheat current (I), respectively. The relationships between selected WASAW process parameters and responses namely tensile strength (TS), yield strength (YS), percentage elongation (E), and weld metal hardness (H) has been established with statistical regression analysis and ANOVA method. Then, the effect of each input factor and their interactions on selected responses was analyzed using single effect and 3D surface plots. Lately, the fractography analysis of tensile fractured surfaces has been carried out to study the failure in all weld metals. Finally, Jaya and genetic algorithms have been applied to optimize the WASAW performance. Overall, the outcomes reveal that developed WASAW process yields a substantial enhancement in the mechanical behavior of all weld metals.

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Multi-objective optimization of gas metal arc welding parameters and sequences for low-carbon steel (Q345D) T-joints

Cited by 27 publications

References 22 publications

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

Multi-objective path optimization for arc welding robot based on discrete DN multi-objective particle swarm optimization

Investigating the effect of electrode preheating in novel water-cooled advanced submerged arc welding process

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