Research Article
Formation of Fe-6.5wt%Si High Silicon Steel by Double Glow Plasma Surface Metallurgy Technology
Zhong Xu*
,
Jun Huang,
Hongyan Wu,
Rui Chen,
Chengyuan Zhang,
Zaifeng Xu,
Weixin Zhang,
Lei Hu,
Bin Zhang
Issue:
Volume 14, Issue 2, April 2026
Pages:
18-24
Received:
18 March 2026
Accepted:
7 April 2026
Published:
14 April 2026
Abstract: Fe-6.5wt%Si high silicon steel is recognized as an optimal magnetic material due to its low iron loss, near-zero magnetostriction, and high saturation magnetization, offering significant advantages in energy savings, weight reduction, and miniaturization of electrical equipment. However, its high brittleness presents substantial challenges for conventional manufacturing processes, and large-scale production remains a global challenge. This paper reports the successful preparation of Fe-6.5wt%Si high silicon steel using the Double Glow Plasma Surface Metallurgy Technology, also referred to as the Xu-Tec process. In this method, a dual-electrode glow discharge configuration is employed within a vacuum vessel, where silicon is sputtered from a pure source cathode and deposited onto a low-silicon steel workpiece cathode, followed by inward diffusion under argon ion bombardment at elevated temperatures. Through systematic optimization of process parameters—including source voltage, workpiece voltage, argon pressure, treatment temperature, and holding time—both homogeneous and gradient high silicon steels were successfully fabricated. Microstructural characterization and compositional analysis revealed that the homogeneous Xu-Tec high silicon steel achieved an average cross-sectional silicon content exceeding 6.5 wt%, while the gradient variant exhibited controlled silicon distribution from the surface to the core. Notably, the thickness of the Xu-Tec processed samples was twice that of the Japanese JNEX 900 and JNHF 600 products, indicating superior diffusion efficiency. The Xu-Tec process is simple, environmentally friendly, and free from corrosion and pollution, offering a promising new route for the large-scale production of high silicon steel. This study provides a foundational basis for future application research and industrialization efforts.
Abstract: Fe-6.5wt%Si high silicon steel is recognized as an optimal magnetic material due to its low iron loss, near-zero magnetostriction, and high saturation magnetization, offering significant advantages in energy savings, weight reduction, and miniaturization of electrical equipment. However, its high brittleness presents substantial challenges for conv...
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Research Article
Optimization in Machine Learning for Application in High Energy Physics: Processing and Anomaly Detection
Issue:
Volume 14, Issue 2, April 2026
Pages:
25-31
Received:
14 February 2026
Accepted:
22 April 2026
Published:
16 May 2026
DOI:
10.11648/j.ajpa.20261402.12
Downloads:
Views:
Abstract: High-energy physics (HEP) experiments generate extraordinarily large and complex datasets, posing significant challenges for real-time data analysis, event reconstruction, and reliable anomaly detection. Traditional analytical techniques often struggle to scale efficiently or fully exploit the rich structure of these data. In this context, machine learning (ML) has emerged as a transformative paradigm, offering powerful tools to enhance computational efficiency, precision, and adaptability in HEP data processing pipelines. This review provides a comprehensive overview of the integration of ML techniques in HEP, with a particular focus on their role in optimizing data analysis workflows and improving experimental performance. We examine a broad spectrum of ML approaches, including supervised and unsupervised learning methods, deep learning architectures, and ensemble models, highlighting their applications in tasks such as signal–background discrimination, feature extraction, noise reduction, and anomaly detection. Special attention is given to advanced algorithms designed for real-time data processing, which are critical for trigger systems and online event selection in modern collider experiments. The effectiveness of these methods is evaluated in the context of large-scale HEP datasets, demonstrating strong performance with metrics including an accuracy of 0.9421, sensitivity of 0.9314, specificity of 0.9507, precision of 0.9458, an F1-score of 0.9386, and an area under the ROC curve (AUC) of 0.9723. By critically analyzing current ML models and their integration into established HEP data analysis frameworks, this review identifies recent advancements, ongoing challenges related to model interpretability, scalability, and robustness, and promising directions for future research. The findings underscore the pivotal role of ML in advancing data-driven discoveries in HEP and support the development of more accurate, efficient, and scalable experimental analyses.
Abstract: High-energy physics (HEP) experiments generate extraordinarily large and complex datasets, posing significant challenges for real-time data analysis, event reconstruction, and reliable anomaly detection. Traditional analytical techniques often struggle to scale efficiently or fully exploit the rich structure of these data. In this context, machine ...
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