Explainable Machine Learning and Process Optimization for Tensile Strength Prediction in Laser Powder Bed Fusion Manufacturing

Laser Powder Bed Fusion (LPBF) is one of the most established metal Additive Manufacturing technologies, yet obtaining predictable links between various process parameters and material properties is still difficult because of the complex and nonlinear nature of LPBF processing. In this study, an Explainable Machine learning framework is presented to predict and optimise the material performance in LPBF using the input parameters (grain size, hatch width, laser speed, layer thickness, and laser power). The total of 1000 LPBF samples developed from experimentally obtained samples was used for model development using Generative Adversarial Network (GAN) based augmentation. Three models: Linear Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) were tested to predict tensile strength and surface roughness. The model performance was evaluated by the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and five fold cross validation. The use of Explicable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP), were used to explain how models worked and to determine which process variables were most important. For this, another optimization system was developed based on a machine-learning framework to identify process conditions that maximize tensile strength. RF model showed the highest performance in the prediction of tensile strength with a mean CV R2 of around 0.80 which demonstrated a good prediction skill. Conversely, surface roughness did not exhibit good predictive power with the highest R2 value for the model being around 0.19. Explainability analysis has always shown laser power to be the most significant parameter which has influence on tensile strength. The optimization results showed that the process window, which was associated with the maximum predicted tensile strength of 475.69 MPa, was calculated. The proposed framework illustrates opportunities for explainable machine learning for LPBF process prediction, interpretation and optimization aiding in data-driven manufacturing decision making.