Enhancing 3D Printing Infill Quality through Advanced Machine Learning

As 3D printing technology revolutionizes manufacturing processes, the quest for improving print quality remains paramount. This research paper explores novel avenues for enhancing infill quality in 3D printing by applying advanced machine-learning techniques. Traditional infill patterns often face structural integrity and overall print performance challenges. Leveraging machine learning algorithms, we propose a comprehensive approach to optimize infill patterns based on critical parameters such as printing speed, Acceleration, and material utilization. This study underscores the transformative potential of artificial intelligence in the realm of 3D printing, paving the way for more innovative and sustainable manufacturing practices. The research establishes algorithms to support quality control for optimizing 3D printing parameters, predicting mechanical performance, and evaluating 3D-printed products. We allow the end-user and practitioner to reduce the calibration (ramp-up) time to obtain the part with good infill quality and improve the productivity of part production.