Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool

Abstract

Carbon-reinforced nylon composite material (PA12CF20), i.e., polyamide, has been widely used in the automotive, medical, and electronics industries for fabricating functional parts, especially in gear manufacturing, due to its excellent wear property. The present work investigates the optimal parameters of the fused filament fabrication (FFF) 3D printing technique using PA12CF20 as feedstock to fabricate parts for enhancing the sliding wear performance. The test specimens are fabricated as per the American society for testing and materials (ASTM) G99 standard. The hybrid heuristic tool, i.e., genetic algorithm-adaptive neuro fuzzy inference system (GA-ANFIS), has been used to train, predict, and optimize the sliding wear performance of the fabricated parts using carbon reinforced polyamide composite material. The experimental runs have been performed on 30 combinations of input factors based on face centered central composite design (FCCCD) and further used for training and optimization for the hybrid GA-ANFIS approach. Scanning electron microscopy has been used to characterize the wear crack of the PA12CF20. It is observed that the optimal parameters retrieved using GA-ANFIS have increased the wear performance of PA12CF20 fabricated specimens, and the same has been validated experimentally. Further, a lathe machine spur gear was fabricated using optimal parameters of the FFF technique and checked the suitability for end-use applications.