Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm

Babu, K. Kamal; Panneerselvam, K.; Sathiya, P.; Haq, A. Noorul; Sundarrajan, S.; Mastanaiah, P.; Murthy, C. V. Srinivasa

doi:10.1007/s00170-017-0897-6

Cited by 73 publications

(20 citation statements)

References 47 publications

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“…The ANN models were further integrated with GA optimization to find optimum parameter combination. Babu et al [18] employed ANN for modelling of mechanical and corrosion properties of weld and optimized FSW variables using GA during welding cryorolled AA2219 alloy. Maji and Pratihar [19] combined GA with adaptive network-based fuzzy interface system (ANFIS) for forward and reverse mapping of EDM process.…”

Section: Application Of Soft Computing Approaches In Welding Optimizamentioning

confidence: 99%

Development of ANN modelling for estimation of weld strength and integrated optimization for GTAW of Inconel 825 sheets used in aero engine components

Choudhury

Chandrasekaran

Devarasiddappa

2020

J Braz. Soc. Mech. Sci. Eng.

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Nickel-based superalloys are widely used in fabrication of components in aero space and nuclear sectors due to excellent strength, corrosion resistance, good ductility and high-temperature resistance. Inconel 825 superalloy is predominantly used for making aircraft engine components. This work investigates single pass welding of Inconel 825 strips employing gas tungsten arc welding. Four weld parameters, viz. welding speed (V), welding current (I), arc length (N) and gas flow rate (GFR), were used to investigate ultimate tensile strength (UTS) of weld employing Box-Behnken design having 27 experiments. The welding current is found the most dominating factor followed by welding speed (V). Higher heat input with low welding speed increases deposition rate that ensures more strength in weldment. Microstructural analysis shows two different grain boundaries, i.e., solidification sub-grain boundaries and solidification grain boundaries, in fusion zone. Artificial neural network (ANN) modelling for UTS is developed, and its predictive capability is compared with multiple regression analysis. A new integrated ANN-TLBO-based soft computing modelling and optimization approach is proposed for obtaining optimum weld parameters to maximize weld strength. The proposed optimization methodology obtained maximum UTS of 701.73 MPa at optimal weld parameters I = 120 A, V = 180 mm/min, GFR= 12 l/min and N = 2.24 mm. Validation result was found highly encouraging with 0.60% error with confirmation experiment. The proposed method has better convergent capability with minimum number of iterations.

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Section: Application Of Soft Computing Approaches In Welding Optimizamentioning

confidence: 99%

Development of ANN modelling for estimation of weld strength and integrated optimization for GTAW of Inconel 825 sheets used in aero engine components

Choudhury

Chandrasekaran

Devarasiddappa

2020

J Braz. Soc. Mech. Sci. Eng.

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“…Many intelligent optimization algorithms based on natural phenomena or natural processes were applied to the processing optimization, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and bat algorithm (BA) [13]. The artificial neural network method was combined with these genetic algorithms to optimize production process parameters, and it achieved good application effects [14]. In order to speed up the convergence speed, it will also be designed for the characteristics of the algorithm itself besides gradient descent.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Yan

2021

Metals

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Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

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“…This problem can be solved successfully by the hybrid optimization method of artificial neural network (ANN) and intelligent optimization algorithm (IOA) which has a good global search ability. Due to the excellent generalization and nonlinear mapping abilities, ANN with a fast learning convergence speed can automatically and accurately deal with the laws that are difficult to analyze, and the established model has a high accuracy and fault tolerance . Therefore, the mapping relationship between the welding process parameters and the joint quality space can be accurately described by the ANN.…”

Section: Introductionmentioning

confidence: 99%

“…At present, the combination of ANN and IOA has successfully realized the optimization of welding process parameters of resistance spot welding, diffusion welding, FSW, and so on . For FSW of dissimilar materials, there are few studies and most of them focus on the inherent welding machine parameters, such as rotating velocity, welding speed, axial force, etc .…”

Section: Introductionmentioning

confidence: 99%

Improving the Mechanical Property of Dissimilar Al/Mg Zn‐Added Ultrasound‐Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network–Gray Wolf Optimization Algorithm

Song

Ren

et al. 2019

Adv Eng Mater

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Process parameters of rotating velocity, welding speed, Zn interlayer thickness, and ultrasound power are optimized by the hybrid of back propagation neural network (BPNN) and gray wolf optimization algorithm (GWOA) to obtain a high‐quality Zn‐added ultrasound‐assisted friction stir lap welding joint of 7075‐T6 Al/AZ31B Mg dissimilar alloys. The results state that the prediction accuracy of the trained BPNN model is acceptable. The optimal process parameters combination is obtained by the GWOA which is combined with the trained BPNN. The verification tests are performed under the executable optimal solution, which consists of the rotating velocity of 1054 rpm, the welding speed of 54 mm min−1, the Zn interlayer thickness of 0.05 mm, and the ultrasound power of 1568 W. The tensile shear load of the joint reaches 9.05 kN, and the strength is 11.8% larger than that of the reported optimal joint. The artificial intelligence optimization method of GWOA combined with BPNN can accurately predict and optimize the joint strength, which has great time and economic advantages.

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Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm

Cited by 73 publications

References 47 publications

Development of ANN modelling for estimation of weld strength and integrated optimization for GTAW of Inconel 825 sheets used in aero engine components

Development of ANN modelling for estimation of weld strength and integrated optimization for GTAW of Inconel 825 sheets used in aero engine components

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Improving the Mechanical Property of Dissimilar Al/Mg Zn‐Added Ultrasound‐Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network–Gray Wolf Optimization Algorithm

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