<ul><li>This paper presents the first comprehensive literature review of deep learning (DL) applications in additive manufacturing (AM).</li><li>The review covers three major areas of AM: design for AM, AM modeling, and monitoring and control in AM.</li><li>The analysis reveals a trend towards using deep generative models for generative design in AM, and incorporating process physics into DL models to improve AM process modeling.</li><li>The paper summarizes the current challenges and recommends areas for further investigation, including generalizing DL models for a wide range of geometry types and incorporating deep generative models to address limited and noisy AM data.</li></ul>

Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges

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