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<p>Equal Channel Angular Pressing (ECAP) and Non-Equal Channel Angular Pressing (NECAP) are severe plastic deformation (SPD) techniques that produce ultrafine-grained materials with superior strength, corrosion resistance, and ductility. While ECAP achieves grain refinement through simple shear in uniform channels, NECAP’s asymmetric design enables higher strain per pass, reducing processing cycles. Despite their advantages, both techniques face challenges, such as complex die design and the need for systematic investigations to optimize process parameters and evaluate industrial scalability. This review article comprehensively highlights the latest developments in ECAP and NECAP technologies, focusing on their mechanisms, optimization of process parameters, and the resulting mechanical and microstructural properties. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into SPD processes has opened up new avenues for optimizing these techniques, enhancing process design, providing predictive insights, and reducing experimental costs. The review introduces the transformative role of ML in advancing SPD technologies, bridging the gap between experimental research and computational modeling. This article offers a path for further study and innovation. It highlights the synergistic combination of conventional manufacturing techniques and ML-driven methodologies to improve the performance and scalability of ECAP and NECAP processes.</p>

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A review of severe plastic deformation via ECAP and variants: insights into process mechanics, material properties, and machine learning optimization

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Fatma Elplacy,
  • Magdy Samuel,
  • Rania Mostafa

摘要

Equal Channel Angular Pressing (ECAP) and Non-Equal Channel Angular Pressing (NECAP) are severe plastic deformation (SPD) techniques that produce ultrafine-grained materials with superior strength, corrosion resistance, and ductility. While ECAP achieves grain refinement through simple shear in uniform channels, NECAP’s asymmetric design enables higher strain per pass, reducing processing cycles. Despite their advantages, both techniques face challenges, such as complex die design and the need for systematic investigations to optimize process parameters and evaluate industrial scalability. This review article comprehensively highlights the latest developments in ECAP and NECAP technologies, focusing on their mechanisms, optimization of process parameters, and the resulting mechanical and microstructural properties. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into SPD processes has opened up new avenues for optimizing these techniques, enhancing process design, providing predictive insights, and reducing experimental costs. The review introduces the transformative role of ML in advancing SPD technologies, bridging the gap between experimental research and computational modeling. This article offers a path for further study and innovation. It highlights the synergistic combination of conventional manufacturing techniques and ML-driven methodologies to improve the performance and scalability of ECAP and NECAP processes.