Post-heat treatment with mechanical impact predictive analysis using fibre-reinforced polyamide 12 composites in 3D printing

Additively manufactured parts are distinctly influenced by diverse 3D-printing parameters, which directly affect their mechanical properties and overall performance. Key factors such as print orientation, infill density, nozzle temperature, and layer height play an essential role in determining the strength, durability, and dimensional accuracy of the printed parts. These parameters must be carefully optimized to get the appropriate mechanical characteristics and ensure that the final product meets quality and performance standards. This research investigates the mechanical behaviour of boron Fiber-reinforced glass bead-filled polyamide 12, focusing on fracture mechanics across various printing orientations. To optimize the composite formulation, this mechanical impact predictive analysis integrates deep learning and advanced optimization techniques, specifically Enhancing Spiking Neural Networks with Hybrid Top-Down Attention and Clouded Leopard Optimization (ESNN-HTDA-CLO). Additionally, post-heat treatment improves tensile strength and surface roughness, achieving nearly a 10% improvement. Also, this research aims to address challenges in enhancing stiffness and temperature resistance while showcasing the composite advantages. The findings underscore the potential of the developed composite in aerospace, automotive, electronics, construction industries, medical devices, and advanced manufacturing.