Springback in sheet metal bending: A comprehensive review of influencing factors, prediction methods, monitoring techniques and compensation strategies
Department of Artificial Intelligence and Robots Engineering, College of Engineering, Al-Karkh University of Science, Baghdad, Iraq.
Review
Open Access Research Journal of Engineering and Technology, 2026, 11(01), 052–070.
Article DOI: 10.53022/oarjet.2026.11.1.0057
Publication history:
Received on 21 June 2026; revised on 26 July 2026; accepted on 29 July 2026
Abstract:
Springback is one of the most significant challenges in sheet metal bending because it adversely affects dimensional accuracy, product quality, and manufacturing efficiency. Its occurrence is governed by the complex interaction of material properties, geometrical characteristics, and forming conditions, making accurate prediction and effective compensation difficult, particularly for advanced high-strength steels and lightweight alloys. This review provides a comprehensive overview of the current state of research on springback in sheet metal bending processes, covering the fundamental mechanisms of elastic recovery, the principal factors influencing springback behavior, and recent advances in prediction, measurement, monitoring, and compensation techniques. Conventional analytical models, finite element analysis (FEA), and emerging artificial intelligence (AI)-based approaches are critically compared in terms of prediction capability, computational efficiency, advantages, limitations, and industrial applicability. In addition, recent developments in optical and laser-based measurement systems, intelligent monitoring technologies, and closed-loop compensation strategies are reviewed to highlight their contributions to improving process accuracy and productivity. The review further discusses the transition toward data-driven and physics-informed modeling, emphasizing the integration of machine learning, digital twins, explainable AI, and real-time sensing within intelligent manufacturing environments. Finally, current research challenges, including computational cost, material uncertainty, model generalization, and real-time implementation, are analyzed, and future research directions are proposed. The findings indicate that hybrid frameworks integrating physics-based modeling, AI, and adaptive process control represent the most promising pathway toward robust, autonomous, and high-precision springback prediction and compensation in next-generation sheet metal manufacturing systems.
Keywords:
Springback; Sheet metal bending; Finite element analysis; Springback prediction; Springback compensation; Intelligent manufacturing
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Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0