Research on the Crack Predictions in K465 Superalloy by Laser Remelting Based on BP Neural Networks

Xinghua, 王杏华 Wang; Lin, Xin; Haiou, 杨海欧 Yang; Qiuge, 李秋歌 Li; Hua, 谭华 Tan; Jiajun, 韩加军 Han; Weidong, 黄卫东 Huang

doi:10.3788/al20143401.0009

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2014

DOI: 10.3788/al20143401.0009

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Research on the Crack Predictions in K465 Superalloy by Laser Remelting Based on BP Neural Networks

王杏华 Wang Xinghua¹,

Xin Lin²,

杨海欧 Yang Haiou³

et al.

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Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network

Huang

Wang

et al. 2019

Coatings

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This paper aimed to establish a nonlinear relationship between laser cladding process parameters and the crack density of a high-hardness, nickel-based laser cladding layer, and to control the cracking of the cladding layer via an intelligent algorithm. By using three main process parameters (overlap rate, powder feed rate, and scanning speed), an orthogonal experiment was designed, and the experimental results were used as training and testing datasets for a neural network. A neural network prediction model between the laser cladding process parameters and coating crack density was established, and a genetic algorithm was used to optimize the prediction results. To improve their prediction accuracy, genetic algorithms were used to optimize the weights and thresholds of the neural networks. In addition, the performance of the neural network was tested. The results show that the order of influence on the coating crack sensitivity was as follows: overlap rate > powder feed rate > scanning speed. The relative error between the predicted value and the experimental value of the three-group test genetic algorithm-optimized neural network model was less than 9.8%. The genetic algorithm optimized the predicted results, and the technological parameters that resulted in the smallest crack density were as follows: powder feed rate of 15.0726 g/min, overlap rate of 49.797%, scanning speed of 5.9275 mm/s, crack density of 0.001272 mm/mm2. Therefore, the amount of crack generation was controlled by the optimization of the neural network and genetic algorithm process.

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Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network

Huang

Wang

et al. 2019

Coatings

View full text Add to dashboard Cite

show abstract

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Research on the Crack Predictions in K465 Superalloy by Laser Remelting Based on BP Neural Networks

Cited by 1 publication

References 0 publications

Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network

Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network

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