In order to predict defects, improve performance, and streamline operations, machine learning techniques are becoming ever more indispensable in manufacturing processes, mainly in sheet metal forming. Incorporating neural networks into the process of sheet metal forming is the subject of this article's exhaustive examination of recent developments and applications. Exploring datasets from a variety of sheet metal forming processes, numerous machine learning models, including ensemble and single learning techniques are investigated. The functionality of this method extends to various tasks, including the prediction of springback in cold-rolled anisotropic steel sheets. The review provides a conclusion section that presents the main implementation methodologies and how they address to some manufacturing issues.
