Three-dimensional (3D) printing has become a widely used manufacturing technology, but predicting the mechanical and physical properties of printed parts remains a critical challenge. In particular, tensile strength, hardness, and surface roughness are essential for assessing product quality and reliability. Addressing this gap requires advanced computational approaches. Machine Learning (ML) algorithms have the potential to enhance automation and provide accurate predictions of product quality in 3D printing. The primary objective of this study is to review, classify, and analyze recent research on the application of ML algorithms for predicting mechanical (tensile strength, hardness) and physical (surface roughness) properties in 3D-printed materials. This review aims to identify current research trends, highlight existing gaps in the literature, and propose potential directions for future investigations in this rapidly evolving interdisciplinary field. For this purpose, a literature review was conducted in the Web of Science database using keywords such as “3D printing”, “machine learning”, “tensile strength”, “hardness”, and “roughness. The review systematically examines the material types, ML algorithms, printing parameters, and testing methods reported in the literature, and the frequency of these parameters is visualized through graphs to illustrate research trends. The findings reveal that ML algorithms achieve high accuracy in predicting tensile strength, hardness, and surface roughness, providing valuable insights for process optimization. However, current research still shows limited evidence for direct improvements in efficiency and error reduction, indicating important directions for future work.
Aktepe et al. (Fri,) studied this question.