With the continuous development of additive manufacturing technology

more and more additively manufactured materials and parts are used in aerospace

automotive manufacturing

medical devices and other fields. However

traditional mechanical property evaluation methods are difficult to effectively assess the complex mechanical properties of additively manufactured materials and parts due to the time-consuming experiments

high cost and limited data volume. Machine learning technology provides a novel and efficient solution for mechanical property evaluation of additively manufactured materials and parts through efficient data processing

multivariate analysis and feature extraction. This paper reviews recent research advances in machine learning for the evaluation of mechanical properties of additively manufactured materials and parts. First

the challenges of additive manufacturing technology in mechanical property evaluation are introduced. Then

specific applications of machine learning in the evaluation of tensile

compressive

fatigue and creep properties as well as fracture toughness are explored. Machine learning methods effectively overcome the limitations of traditional methods by improving prediction accuracy

reducing experimental costs

and accelerating evaluation. Finally

several challenges and pending issues in the application of machine learning in additive manufacturing are enumerated

and future research directions are envisioned.