This paper aimed to establish a nonlinear relationship between laser cladding process\nparameters and the crack density of a high-hardness, nickel-based laser cladding layer, and to\ncontrol the cracking of the cladding layer via an intelligent algorithm. By using three main process\nparameters (overlap rate, powder feed rate, and scanning speed), an orthogonal experiment was\ndesigned, and the experimental results were used as training and testing datasets for a neural\nnetwork. A neural network prediction model between the laser cladding process parameters and\ncoating crack density was established, and a genetic algorithm was used to optimize the prediction\nresults. To improve their prediction accuracy, genetic algorithms were used to optimize the weights\nand thresholds of the neural networks. In addition, the performance of the neural network was\ntested. The results show that the order of influence on the coating crack sensitivity was as follows:\noverlap rate > powder feed rate > scanning speed. The relative error between the predicted value\nand the experimental value of the three-group test genetic algorithm-optimized neural network\nmodel was less than 9.8%. The genetic algorithm optimized the predicted results, and the\ntechnological parameters that resulted in the smallest crack density were as follows: powder feed\nrate of 15.0726 g/min, overlap rate of 49.797%, scanning speed of 5.9275 mm/s, crack density of\n0.001272 mm/mm2. Therefore, the amount of crack generation was controlled by the optimization\nof the neural network and genetic algorithm process.
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