Artificial Intelligence for Optimizing 3D Printing Quality, Time, and Material Use

Additive manufacturing (AM) has evolved from a prototyping tool into a key production technology across aerospace, healthcare, and automotive sectors, yet persistent issues in dimensional accuracy, surface quality, material waste, and print time still limit its wider adoption. This research investigates how artificial intelligence (AI)-driven optimization can improve the accuracy, efficiency, and quality of 3D-printed parts by jointly optimizing build orientation and support structures. Building on an extensive review of the main AM technologies (FDM, SLA, SLS) and recent advances in AI for manufacturing, the study synthesizes evidence on how machine learning, deep learning, and reinforcement learning can be used to predict print defects, select near-optimal orientations, and design minimal yet stable support topologies. The analysis highlights that AI-based predictive models can reduce trial-and-error in parameter selection, enhance dimensional fidelity, and mitigate common defects such as warping, poor layer adhesion, and surface roughness. Furthermore, AI-optimized support generation can significantly decrease material consumption, post-processing effort, energy use, and overall printing time, thereby improving both economic performance and environmental sustainability. The research consolidates these insights into a conceptual framework for AI-augmented 3D printing, outlining data requirements, learning workflows, and feedback-control loops needed for closed-loop, self-optimizing print systems. Finally, it discusses current barriers to industrial deployment, including data scarcity, computational cost, and the limited accessibility of advanced AI tools for small and medium-sized enterprises, and identifies promising research directions such as user-friendly AI interfaces, technology-specific models, and sustainability-oriented optimization objectives. Overall, the work demonstrates that integrating AI with additive manufacturing offers a viable pathway towards more accurate, efficient, and sustainable 3D printing processes. The proposed framework serves as a roadmap for researchers and practitioners aiming to design robust, autonomous, and resource-efficient AM workflows