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Achieving High Hardness and Uniformity in Fe-Based Amorphous Coatings for Enhanced Wear Resistance via Explainable Machine Learning

文献类型：期刊论文

作者Zhang, Enhao1; Ma, Cong1; Yuan, Jiachi1; Yan, Shuang1; Zhang, Zhibin1; Jing, Zhiyuan1; Zhang, Binbin2
刊名COATINGS
出版日期2026-02-05
卷号16期号:2页码:25
关键词amorphous alloy coating thermal spraying explainable machine learning data augmentation hardness uniformity
ISSN号2079-6412
DOI10.3390/coatings16020199
通讯作者Zhang, Zhibin(eacbia@163.com) ; Jing, Zhiyuan(jing_zhiyuan@163.com) ; Zhang, Binbin(zhangbinbin@qdio.ac.cn)
英文摘要Highlights What are the main findings? center dot A unified HVAF process optimization framework is proposed by integrating DDPM-based data augmentation with explainable machine learning. center dot DDPM generates synthetic samples with the highest statistical fidelity and distributional consistency, effectively mitigating data scarcity. What are the implications of the main findings? center dot The optimized GBR model, enhanced with 10% DDPM-generated data, achieves superior prediction accuracy and generalization for coating hardness and uniformity. center dot SHAP analysis quantitatively reveals the dominant effect of spraying distance and uncovers coupled mechanisms governing hardness uniformity.Highlights What are the main findings? center dot A unified HVAF process optimization framework is proposed by integrating DDPM-based data augmentation with explainable machine learning. center dot DDPM generates synthetic samples with the highest statistical fidelity and distributional consistency, effectively mitigating data scarcity. What are the implications of the main findings? center dot The optimized GBR model, enhanced with 10% DDPM-generated data, achieves superior prediction accuracy and generalization for coating hardness and uniformity. center dot SHAP analysis quantitatively reveals the dominant effect of spraying distance and uncovers coupled mechanisms governing hardness uniformity.Abstract High-Velocity Air-Fuel (HVAF) spraying of Fe-based amorphous coatings involves strong nonlinear coupling among multiple process parameters, while practical optimization is severely constrained by limited experimental data and poor model interpretability. To address these challenges, a systematic data-driven optimization framework integrating the Denoising Diffusion Probabilistic Model (DDPM)-based data augmentation with explainable machine learning is proposed. Coating microhardness and hardness uniformity were jointly selected as target properties to capture both performance level and spatial reliability. Three generative models-Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and DDPM-were comparatively evaluated using statistical matching and distribution-consistency metrics, revealing that DDPM most faithfully reproduces the intrinsic statistical characteristics of real HVAF process data. We benchmarked ten representative regression algorithms covering classical statistical learning, ensemble methods, and deep learning paradigms, with GBR demonstrating the highest predictive accuracy and stability. The inclusion of 10% DDPM-generated samples further improved the predictive precision of the GBR model. SHapley Additive exPlanations (SHAP) quantitatively identified spraying distance as the dominant parameter governing coating hardness, while elucidating the coupled effects of multiple parameters on hardness uniformity. By interpolatively expanding the process parameter space, a two-stage screening strategy identified 98 high-performance parameter combinations. Experimental validation confirmed that the optimal parameter set simultaneously achieved higher hardness and improved uniformity compared with the original best condition, resulting in a 13.6% reduction in wear rate.
WOS关键词STAND-OFF DISTANCE ; DATA AUGMENTATION ; HVOF ; OPTIMIZATION ; CORROSION ; NETWORKS
资助项目National Natural Science Foundation of China[52275225]
WOS研究方向Materials Science ; Physics
语种英语
WOS记录号WOS:001699977200001
出版者MDPI
源URL[http://ir.qdio.ac.cn/handle/337002/204817]  
专题海洋研究所_海洋腐蚀与防护研究发展中心
通讯作者Zhang, Zhibin; Jing, Zhiyuan; Zhang, Binbin
作者单位1.Acad Mil Sci, Def Innovat Inst, Beijing 100071, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, State Key Lab Adv Marine Mat, Qingdao 266071, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Enhao,Ma, Cong,Yuan, Jiachi,et al. Achieving High Hardness and Uniformity in Fe-Based Amorphous Coatings for Enhanced Wear Resistance via Explainable Machine Learning[J]. COATINGS,2026,16(2):25.
APA Zhang, Enhao.,Ma, Cong.,Yuan, Jiachi.,Yan, Shuang.,Zhang, Zhibin.,...&Zhang, Binbin.(2026).Achieving High Hardness and Uniformity in Fe-Based Amorphous Coatings for Enhanced Wear Resistance via Explainable Machine Learning.COATINGS,16(2),25.
MLA Zhang, Enhao,et al."Achieving High Hardness and Uniformity in Fe-Based Amorphous Coatings for Enhanced Wear Resistance via Explainable Machine Learning".COATINGS 16.2(2026):25.

入库方式： OAI收割

来源：海洋研究所

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