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<p>Electromagnetic pulse technology has emerged as a transformative approach in advanced manufacturing, enabling high-speed, solid-state metal joining and precision forming without heat input or mechanical contact. This review presents a comprehensive synthesis of recent developments in Electromagnetic Pulse Welding (EMPW) and Electromagnetic Forming (EMF), focusing on core principles, process parameters, material compatibility, and industrial applications. A bibliographic analysis using IEEE Xplore, ScienceDirect, and Scopus databases identifies leading contributors, funding agencies, and global research trends. The review highlights significant progress in dissimilar metal joining, lightweight component fabrication, and simulation-driven optimization. In addition, it integrates emerging applications of Artificial Intelligence (AI) and Machine Learning (ML), including neural networks, support vector machines, and digital twin frameworks for real-time monitoring, defect prediction, and adaptive control. Comparative tables, sector-specific case studies, and challenge-resolution mappings are provided to enhance clarity and practical relevance. EMPW/EMF technologies are increasingly recognized as green, scalable, and industry-ready solutions for electric vehicles (EVs), aerospace, and renewable energy systems. Future research directions emphasize AI-enabled process intelligence, coil life prediction, and hybrid forming strategies to accelerate industrial adoption.</p>

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A Bibliographic Review of Electromagnetic Pulsed Welding and Forming Technology

  • 期刊论文

  • 发表日期：

    2026年1月2日

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  • Archana Sharma,
  • Amit Kumar Agrawal

摘要

Electromagnetic pulse technology has emerged as a transformative approach in advanced manufacturing, enabling high-speed, solid-state metal joining and precision forming without heat input or mechanical contact. This review presents a comprehensive synthesis of recent developments in Electromagnetic Pulse Welding (EMPW) and Electromagnetic Forming (EMF), focusing on core principles, process parameters, material compatibility, and industrial applications. A bibliographic analysis using IEEE Xplore, ScienceDirect, and Scopus databases identifies leading contributors, funding agencies, and global research trends. The review highlights significant progress in dissimilar metal joining, lightweight component fabrication, and simulation-driven optimization. In addition, it integrates emerging applications of Artificial Intelligence (AI) and Machine Learning (ML), including neural networks, support vector machines, and digital twin frameworks for real-time monitoring, defect prediction, and adaptive control. Comparative tables, sector-specific case studies, and challenge-resolution mappings are provided to enhance clarity and practical relevance. EMPW/EMF technologies are increasingly recognized as green, scalable, and industry-ready solutions for electric vehicles (EVs), aerospace, and renewable energy systems. Future research directions emphasize AI-enabled process intelligence, coil life prediction, and hybrid forming strategies to accelerate industrial adoption.