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RESEARCHInternational Journal of Material Forming (2026) 19:77 https://doi.org/10.1007/s12289-026-02032-8 Narcis-Nicolae Popescunarcis-nicolae.popescu@student.tuiasi.ro1 Faculty of Materials Science and Engineering, Gheorghe Asachi Technical University of Ia?i, Dimitrie Mangeron 41, Ia?i, Romania2 Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University of Ia?i, Dimitrie Mangeron 27, Ia?i, RomaniaAbstractThis article presents a review of the current state of neural network applications in plastic deformation processes, together with an experimental study and modeling of the electromagnetic forming process of AlMn0.5Mg0.5 aluminum alloy sheet. The fi rst part summarizes several case studies that demonstrate the potential of neural networks in the plastic deformation of metallic and non-metallic materials such as composites, glasses, polymers, foams, clay, as well as shape memory alloys. This part examines dif f erent types of neural networks, learning algorithms, feature selection methods, and optimization techniques that apply to plastic deformation processes. It also explores their integration with other modeling approaches, such as regression and fi nite element analysis. The second part focuses on predicting the maximum deformation depth, which serves as an indicator of formability, in the electromagnetic bulging of round cups. Six process parameters (deformed part size, thickness of specimen, gap distance, number of coil turns, capacitance of capacitor bank and the charging voltage) are identif i ed as signif i cantly inf l uential and serve as input variables for both the experimental design and modeling. The study applies both nonlinear regression and neural networks to predict the output parameter. Both models reliably predict the output parameter, and their performance is demonstrated by an average relative error of 2.53% for the nonlinear regression model and a coef f i cient of determination of 0.9971 for the neural network model, which indicate their potential for manufacturing process control.Keywords Plastic deformation · Neural networks · Review · Electromagnetic forming · Nonlinear regression · Comparative analysesReceived: 8 November 2025 / Accepted: 8 May 2026© The Author(s) 2026A review of neural networks used in plastic deformation of materials and an electromagnetic forming applicationDorin Luca 1  · Florin Leon 2  · Narcis-Nicolae Popescu 1  · Dorian D. Luca 1  · Georgian Artene 2plastic deformation processes, alongside which uncon-ventional processes have subsequently emerged that use concentrated energy sources (explosives), electromagnetic energy, hydraulic or pneumatic pressure media, ultrasound or mechanical vibrations, etc.The development and improvement of metal forming processes used to produce net-shape parts (without further processing), in the current context of green economy and energy crisis, can be achieved by modeling the processes and simulating their virtual development. Information tech-nology, including cloud computing, big data and artif i cial intelligence, has played a decisive role in the intelligent manufacturing of high-performance equipment used in metal forming processes, as well as in engineering man-agement activities for production preparation and tracking, and for the reconstruction of the enterprise ecosystem [1]. Conventional mathematical models, together with computer simulation methods can contribute to predicting the opti-mal parameters (force, degree of deformation, temperature, IntroductionMore than 80% of the world metals and alloys produc-tion is subjected to plastic deformation processing. The parts produced by plastic deformation are used in all industrial fi elds (automotive, naval, aerospace, research equipment, medical equipment, household appliances, etc.). Over time, various metal forming techniques have been developed, which have become known as classical