<b>Optimization</b> <b>and</b> <b>Quality</b> <b>Control</b> <b>of</b> <b>Additives</b><b> </b><b>Manufacturing</b> <b>Based</b> <b>on&l

This paper aims to review the research progress in the optimization and quality control of additive manufacturing methods based on machine learning over the past three years. By systematically reviewing and analysing the relevant contributions, this paper discusses the application of machine learning in additive manufacturing, such as parameter optimization, material performance prediction, real-time process monitoring and data-driven quality evaluation. Machine learning technology significantly improves the stability and product quality of additive manufacturing, but it also faces challenges such as data standardization, multiscale modelling and interdisciplinary cooperation. Finally, this paper proposes the following: research directions and policy recommendations to promote the development of additive manufacturing technology.