Modeling and process parameter optimization of laser cutting based on artificial neural network and intelligent optimization algorithm

Laser cutting technology has proven advantageous in processing high-hardness metals, ceramics, and composites. However, the process parameters significantly influence the kerf and heat-affected zone widths. Therefore, it is necessary to establish an accurate prediction model of laser cutting quality to optimize the process parameters and improve processing quality and efficiency. This work proposes a laser-cutting quality prediction model based on an artificial neural network optimized by the particle swarm optimization algorithm. The particle swarm optimization algorithm is used to optimize the number of nodes in the hidden layer, activation function, initial weights, and biases for a more accurate model. This model considers the effects of average power, repetition frequency, and scan speed on the kerf width, heat-affected width, and processing efficiency. The non-dominated sorting genetic algorithm II is adopted for the process parameter optimization. Finally, the experiments are carried out to verify the model. The results show that the model has a high accuracy with a prediction error of less than 10% for kerf width and heat-affected zone. Moreover, the optimized process parameters meet the given machining targets and increase the machining efficiency by over 40%.