Analytical and Machine‑Learning Framework for Predicting the Impact of LPBF Parameters on the Hardness of Inconel™ 718

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. ANOVA and hardness measurements identified relationships that cannot be predicted using conventional statistical methods. Particle Swarm Optimization and Genetic Algorithm were proposed to define the relationships between the input and output data. Although the mean accuracy was high (97% for Particle Swarm Optimization and 91% for Genetic Algorithm), the drawbacks were calculation variation and sensitivity to parameter changes. 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 Network (ANN), and Random Tree (RT). The low R ² values were observed in the initial implementation with a mean accuracy of 90%. The linear method of optimizing output data increased R ² values up to and beyond 0.9 with high average accuracy. GPR and single-layer ANN performed best in terms of training results. A rollback process was implemented on the test results. GPR and ANN displayed the best results with the highest R ² (0.99) and MAPE (1.3%) values on the testing data, which proved them as the best solutions.