Abstract In Laser Powder Bed Fusion, a layer-by-layer melting of metal powder takes place and is specifically suited to high-performance applications in advanced technologies using Inconel™ 718. Vickers hardness (HV20) of the samples was measured as an output variable of the machine learning model after the heat treatment and surface polishing. Particle swarm optimization and genetic algorithm were proposed to define the relationships between the input and output data. The prediction of hardness was then done using five regression models, such as support vector machine, Gaussian process regression (GPR), single-layer and deep-layer artificial neural networks (ANNs), and random tree (RT). A novel technique, rollback, for optimizing output data increased the accuracy metrics. GPR and ANNs performed best in terms of training results. The rollback process was also implemented on the test results. GPR and ANN displayed the best results, with the highest R 2 , NSE, and KGE values between 0.97 and 0.99, and the lowest MAPE, MAE, and RMSE on the testing data, which proved them as the best solutions. The Kruskal–Wallis test and Taylor diagram were also used to evaluate model performance.
Abyaneh et al. (Mon,) studied this question.