ML-enabled prediction and optimization of tensile strength and surface characteristics in additively manufactured PETG components

This work gives an in-depth discussion on prediction and optimisation of the mechanical properties of FDM-printed polyethylene terephthalate glycol (PETG) parts by applying statistical and machine learning methods. Experimentally, the influence of the three important process variables on the surface roughness (SR) and ultimate tensile strength (UTS) was studied: the melting temperature, the height of the layer, and the infill density. The predictive capacity of the Artificial Neural Network (ANN), Random Forest Regression (RFR) and Response Surface Methodology (RSM) models were developed and compared. RFR model was found to be more accurate than other models with the following values of R 2 = 0.94 (training) and R 2 = 0.83 (testing) and mean absolute error (MAE) = 2.51 when predicting UTS. The mean error between the experimental and predicted values was lower than 5% accounting for the robustness of the developed model. The process parameters were optimised with multi objective Genetic Algorithm (GA) and produced an optimum UTS of 38.39 MPa and a minimum surface roughness of 5.65 um at 82.42% infill density, 0.26 mm layer height, and 230.26 C melting temperature. The findings are also validated by carrying out microstructural study using SEM image. These findings show how the mechanical performance and surface quality of PETG components that are FDM-printed can be enhanced by combining machine learning and evolutionary optimisation.