Prediction and optimization of tensile strength of additively manufactured PEEK biopolymer using machine learning techniques

This research delves into the intersection of machine learning and additive manufacturing, specifically focusing on predicting the mechanical strength of FDM-printed PEEK components. The impact of these components is felt across a variety of industries, including aerospace, biomedical, and automobile where the mechanical strength plays a key role in material selection. Throughout the study, the mechanical strength is investigated through experimental analysis of four process parameters: infill density, layer height, printing speed, and infill pattern. Support Vector Regression (SVR) and Random Forest Regression (RFR) are used to accurately predict the ultimate tensile strength of the printed parts, with an average deviation from the experimental value of less than 5%. The study also examines the mechanical strength variation in relation to the process parameters using contour and surface plots. A genetic algorithm (GA) is employed to optimize the more accurate mechanical strength data predicted by RFR algorithm which yielded a maximum ultimate tensile strength value of 66.17 MPa for 80% infill density, 0.103 mm layer height, 25.001 mm/sec printing speed and octet infill pattern. Microstructural studies and test results further support the outcomes obtained through parametric analysis and optimization.