Study on optimization of laser cladding process parameters of aluminum alloys using a prediction model of the neuralgenetic algorithm

Liang, Mulin; Wang, Chenglei; Cai, Miao; Liang, Chaojie; Xie, Yingguang; Liu, Weijie; Yang, Jijie; Tan, H.S.; Zhou, Shengfeng

doi:10.1088/1742-6596/2044/1/012149

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…In order to optimize the parameters of laser cladding, it is necessary to select an algorithm. Algorithms, according to the fitness function and time density, include Particle Swarm Optimization (PSO) [21], the Genetic Algorithm (GA) [22], the Pareto Evolutionary Algorithm (SPEA) [23], etc. In contrast, the second-generation non-dominated sorting genetic algorithm (NSGA-II) with elite strategy is used to fit the relation between the factor and response, with a non-deflection degree cubic polynomial.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi-Track Laser-Cladding Process of Titanium Alloy Based on RSM and NSGA-II Algorithm

Shu

et al. 2022

Coatings

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Titanium alloy is an important material in the 21st century and its consumption in the aerospace and energy fields is increasing. In the production and repair of titanium alloy, the problem of energy saving and consumption reduction is becoming increasingly important. Laser-cladding technology with optimized parameters can bring great economic benefit. In order to obtain the best process parameters of laser-cladding TC4 alloy powder, a method of laser-cladding parameters’ optimization based on the RSM and NSGA-II Algorithm is proposed. The BBD (Box–Behnken Design) experiment scheme was designed by the response surface method. A surrogate model between input variables (laser power, scanning speed, and powder-feeding speed) and response values (macroscopic quality, microhardness, and average friction coefficient) was established. The second generation non-dominant sorting genetic algorithm (NSGA-II) was used to optimize the process parameters and the optimization results were verified by experiments. The results show that the optimum parameters are a laser power of 2600 W, scanning speed of 19.1 mm/s, and powder-feeding rate of 12.2 g/min. The samples prepared with the best process parameters show mainly abrasive wear, accompanied by a small amount of adhesive wear. Its wear depth is 7.71 μm and the average friction coefficient is 0.293. After cladding, the macroscopic quality of the cladding layer is increased by 5.8%, the microhardness is increased by 10.1%, and the average friction coefficient is reduced by 27.6%.

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Section: Introductionmentioning

confidence: 99%

Optimization of Multi-Track Laser-Cladding Process of Titanium Alloy Based on RSM and NSGA-II Algorithm

Shu

et al. 2022

Coatings

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“…However, the convergence speed of the traditional BP neural network algorithm was found that to the optimal solution is too slow, which can lead to large errors. Genetic algorithm can significantly improve the convergence speed of the optimal solution through global optimization, and the prediction accuracy of experimental results was further improved [29]. Therefore, response surface method (RSM) and genetic neural network algorithm (GA-BP) can not only deal with multivariable experimental parameters, but also deeply analyze the nonlinear coupling relationship between process parameters and surface topography, effectively characterize the target parameters, and provide effective technical support for process optimization and surface topography prediction.…”

Section: Introductionmentioning

confidence: 99%

Surface quality optimization of laser cladding based on surface response and genetic neural network model

Zhang¹,

Xu²,

Sun³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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The surface roughness is a main performance parameter to characterize the quality of the macroscopic morphology in laser cladding multi-lap forming of key parts. In this paper, both the Taguchi and the Box-Behnken Design (BBD) experiments were used to conduct the laser cladding multi-channel lap experiment. The relationship between the process parameters regarding laser power, powder feeding speed, scanning speed, lap rate and the surface roughness of the coating was illustrated via experiments. The neural network model (Genetic Algorithm-Back Propagation, GA-BP) and response surface methodology (RSM) optimized by genetic algorithm were established, respectively. Furthermore, the optimal cladding parameters were optimized, and the coupling effect between the cladding process and the forming quality of the multi-channel lap was revealed. It is found that the root mean square error (RMSE) and absolute average deviation (AAD) of GA-BP model were both smaller than those of the RSM model. The coefficient of determination R-Squared of GA-BP model was closer to 1. Moreover, the minimum roughness of 20.89 μm by optimizing the process parameters through the genetic neural network model was lower than the surface response optimization of 35.67μm. The genetic neural network model optimized by genetic algorithm can improve the prediction accuracy on the surface macro-quality of multi-channel lap cladding layer, which provides a theoretical basis and technological direction for the improvement of the surface quality of key parts.

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Microstructure and properties of AlCoCrFeNiTi high-entropy alloy coatings prepared by laser cladding based on the response surface methodology

Que

Lian

Yao

et al. 2022

Int J Adv Manuf Technol

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Study on optimization of laser cladding process parameters of aluminum alloys using a prediction model of the neuralgenetic algorithm

Cited by 3 publications

References 6 publications

Optimization of Multi-Track Laser-Cladding Process of Titanium Alloy Based on RSM and NSGA-II Algorithm

Optimization of Multi-Track Laser-Cladding Process of Titanium Alloy Based on RSM and NSGA-II Algorithm

Surface quality optimization of laser cladding based on surface response and genetic neural network model

Microstructure and properties of AlCoCrFeNiTi high-entropy alloy coatings prepared by laser cladding based on the response surface methodology

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