INTEGRATING NEURAL NETWORKS INTO SHEET METAL FORMING: A REVIEW OF RECENT ADVANCES AND APPLICATIONS

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 articl...

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Bibliographic Details
Published inJournal of engineering studies and research Vol. 30; no. 1
Main Authors COSMIN - CONSTANTIN GRIGORAȘ, ȘTEFAN COȘA, VALENTIN ZICHIL
Format Journal Article
LanguageEnglish
Published Alma Mater Publishing House "Vasile Alecsandri" University of Bacau 01.07.2024
Subjects
neural network, sheet metal forming, springback compensation
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Summary: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.
ISSN:2068-7559
2344-4932