A Review of Machine Learning Algorithm Development Methods for Predicting the Mechanical Properties of ۳D Printed Components and Optimizing Parameters

Additive Manufacturing (AM) technology has been widely used in various industries due to its high flexibility in designing and producing complex components. However, the precise control of process parameters and the prediction of the mechanical properties of printed components have always been challenging. This paper reviews the role of Machine Learning (ML) in optimizing printing parameters (such as laser power, scan speed, and layer thickness) and predicting the quality of components. Predictive models for defects such as porosity and cracking are also analyzed. A comparison of data-driven approaches, including supervised learning and reinforcement learning, demonstrates that each has unique advantages under specific conditions. Finally, a perspective on the future integration of AM and Artificial Intelligence (AI) in the design of new materials is presented.

کلیدواژه ها:

Additive Manufacturing ، Design and Production of Complex Components ، Process Parameter Control ، Mechanical Properties