Post-Treatment and Hybrid Techniques for Prolonging the Service Life of Fused Deposition Modeling Printed Automotive Parts: A Wear Strength Perspective

This study aims to explore the wear characteristics of fused deposition modeling (FDM) printed automotive parts and techniques to improve wear performance. The surface roughness of the parts printed from this widely used additive manufacturing technology requires more attention to reduce surface roughness further and subsequently the mechanical strength of the printed geometries. The main aspect of this study is to examine the effect of process parameters and annealing on the surface roughness and the wear rate of FDM printed acrylonitrile butadiene styrene (ABS) parts to diminish the issue mentioned above. American Society for Testing and Materials (ASTM) G99 specified test specimens were fabricated for the investigations. The parameters considered in this study were nozzle temperature, infill density, printing velocity, and top/bottom pattern. The hybrid tool, i.e., GA–ANN (genetic algorithm–artificial neural network) has been opted to train, predict, and optimize the surface roughness and sliding wear of the printed parts. Results disclose that the minimum surface roughness obtained with GA–ANN was 1.05482 μm for infill density of 68%, nozzle temperature of 230°C, printing velocity of 80 mm/sec, and for concentric type of top/bottom pattern. In extension of this study, annealing was performed on the specimens printed on the optimized results obtained from the analysis at three different temperatures of 110°C, 150°C, and 190°C and for a fixed period of time of 60 min as a post-treatment process to further study the impact of annealing on the surface roughness and wear rate. The surface roughness of the samples showed a discernible improvement as a result of annealing, which can further make significant inroads in automotive industries.