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Prof. Shaofeng Wang

Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Sichuan Mianyang 621010, China

Co-Affiliations:School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Research Publications & English Decoded Briefs

Showing 3 publications
Surface Technology (表面技术)2026DOI: 10.16490/j.cnki.issn.1001-3660.2026.10.002

Research Progress and Prospects of Corrosion-resistant High-entropy Alloy Coatings

Marine environments impose combined electrochemical, microbiological, and cavitation erosion degradation on metallic infrastructure, shortening service intervals and inflating maintenance expenditure. High-entropy alloy (HEA) coatings mitigate these failure modes through simple solid-solution or amorphous microstructures that suppress galvanic coupling and promote dense passive film formation. This review systematically examines corrosion-resistant HEA coatings from single-factor to multi-factor coupling perspectives, covering classification and compositional design, fabrication routes, and corrosion behavior under complex marine conditions. Key coating systems include FeCoCrNiMn, AlCoCrFeNi, FeCrNiCoAl, and (FeCoCrNi)75B15Si10 amorphous alloys deposited by atmospheric plasma spraying, high-velocity oxy-fuel spraying, and wire arc spraying. Elemental additions of Cr, Al, and Mo enhance passivation; B and Si promote amorphous phase formation. The review identifies core engineering bottlenecks: compositional design, process optimization, and service performance validation. A multi-scale simulation, process-structure optimization, and in-situ characterization framework is proposed to accelerate coating deployment. These findings provide theoretical and technical guidance for next-generation corrosion-resistant coatings in marine equipment.

Atomic Energy Science and Technology (原子能科学技术)2025DOI: 10.7538/yzk.2025.youxian.0449

Structural Influence on Radiation-induced Single-event Effects in SiC MOSFETs: Comparative Analysis of Planar and Trench Designs

The single-event susceptibility of three silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) power devices structures (planar, trench and double trench) is researched by the technology computer-aided design (TCAD) simulation. Comparative analysis of the heavy-ion irradiation effects on three device structures reveals distinct susceptibility characteristics. The gate oxide region is identified as the most sensitive position in planar devices, while trench and double-trench structures exhibit no localized sensitive regions. Furthermore, the single-event susceptibility demonstrates strong depth dependence across all three structures, with enhanced vulnerability observed at greater ion penetration depths.

Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报)2025DOI: 10.1007/s12613-025-3110-1

Strength prediction and cuttability identification of rock based on monitoring while cutting (MWC) using a conical pick

Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks. This study proposes an intelligent approach for predicting rock strength and cuttability. A database comprising 132 data sets is established, containing cutting parameters (such as cutting depth and pick angle), cutting responses (such as specific energy and instantaneous cutting rate), and rock mechanical parameters collected from conical pick-cutting experiments. These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies. In addition, rock cuttability is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method, and subsequently identified through machine learning approaches. Various models are compared to determine the optimal predictive and classification models. The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model, and the optimal model for rock cuttability classification is the radial basis neural network model.

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