Abstract
Abstract
The purpose of this research is to investigate the correlation between statistical and machine learning techniques and additive manufacturing, with a specific focus on predicting the Rockwell hardness of FDM-printed polyether ether ketone (PEEK) components. These components have a significant impact on various industries, such as aerospace, biomedical, and automobile. The study analyzes the hardness by conducting experimental analysis of four process parameters, including infill density, layer height, printing speed, and infill pattern. The research utilizes Response Surface Methodology (RSM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN) to accurately predict the Rockwell hardness of the printed parts, with an average deviation of less than 5% from the experimental value. The study also investigates how hardness varies with FDM process parameters using contour and surface plots. Furthermore, the study utilizes RNN integrated with the Particle Swarm Optimization (PSO) algorithm to optimize Rockwell hardness. This approach achieved a peak Rockwell hardness value of 66.89 RHN under conditions of 80% infill density, 0.1mm layer height, 25 mm sec−1 printing speed, and an octet infill pattern. Microstructural examinations and test results corroborate the findings derived from parametric analysis and optimization efforts.