Process optimization of laser melting deposition for DZ125L based on machine learning

Purpose The purpose of this paper is to accurately predict and optimize the process parameters of laser metal deposited Ni-based superalloys using machine learning (ML). Design/methodology/approach Orthogonal experiments were carried out to obtain the raw data and determine the effects of parameters on the geometric characteristics of the deposition layer. A support vector regression (SVR) model based on the Crested Porcupine Optimizer (CPO) algorithm for predicting the geometric characteristics of the deposition layer is proposed. The Multi-objective Red-billed Blue Magpie Optimizer (MORBMO) algorithm was adopted to optimize the process parameters. Findings Laser power has the greatest effect on depth and width, and scanning speed has the most pronounced impact on height. Both scanning speed and powder feeding rate have a significant impact on the wetting angle. Compared with the classical SVR model, the CPO-SVR improves the R2 values of height, depth, width and wetting angle by 20.72%, 20.16%, 11.27% and 17.78%, respectively, and reduces the MAPE by 36.43%, 8.53%, 3.95% and 9.83%, respectively. The optimized parameters were determined to be a laser power of 1165 W, a scanning speed of 9 mm/s and a powder feed rate of 0.8 r/min. Then, the relative density of the bulk specimen fabricated using these parameters was increased by 1.03% compared to that prepared with the process parameters optimized by previous process experiments. Originality/value The findings of this study demonstrate the potential of the proposed ML method based on CPO-SVR and MORBMO algorithms for the prediction and optimization of laser-melting-deposited Ni-based superalloys.