目的 开发一种机器学习辅助设计框架,用于优化激光熔覆Stellite-6涂层的工艺参数,旨在实现机械性能和耐腐蚀性能的协同增强,以满足深地工程的潜在应用需求。方法 建立了包含36组激光参数的完整数据集,采用显微硬度与电化学性能作为评估涂层质量的主要指标。引入了多元高斯数据扩充方法,有效提高了数据集的多样性和模型的泛化能力。对4种非线性回归算法（LSTM、BPNN、GBT和SVM）进行了评估,其中LSTM模型的预测精度最高（R2=0.907,RMSE=9.65）。将遗传算法（GA）与LSTM模型相结合以实现工艺参数的多目标优化,在最大化显微硬度的同时最小化腐蚀电流密度。结果 采用优化后的工艺参数成功制备出了具有致密微观结构、γ-Co晶粒细化、富铬碳化物均匀分布的涂层。实验表明,LSTM + GA算法对显微硬度和自腐蚀电流密度的实验预测误差在2.3%~5.5%之间,具有良好的预测能力。优化后的涂层显微硬度为601HV0.2,自腐蚀电流密度为2.439×10-6 A/cm2,具有优异的耐磨性和耐腐蚀性。结论 本研究凸显出将机器学习与激光加工相结合,实现表面工程系统智能化、数据驱动优化的潜力,展现其在表面工程应用领域的巨大潜力。
Abstract
A data-driven framework integrating machine learning and laser cladding is proposed to achieve coordinated optimization of mechanical and electrochemical performance in Stellite-6 coatings deposited on 42CrMo steel. Unlike conventional trial-and-error parameter selection, the work aims to establish a quantitative mapping between processing parameters and coating performance by combining experimental design, statistical data augmentation, and hybrid optimization algorithms. A dataset consisting of 36 experimental conditions was constructed by systematically varying laser power (800-1 800 W) and scanning speed (350-600 mm/min), while maintaining constant powder feed rate (14 g/min) and overlap ratio (40%). Microhardness and corrosion current density were selected as dual performance indicators to capture the trade-off between strengthening and corrosion resistance. To overcome the limitation of small sample size, a multivariate Gaussian data augmentation strategy was introduced, generating 164 synthetic samples while preserving the statistical structure and physical correlations of the original dataset. Compared with linear interpolation and SMOTE approaches, Gaussian augmentation demonstrated superior capability in maintaining covariance consistency and avoiding artificial clustering, thereby improving model generalization. Four nonlinear regression models, including LSTM, BPNN, GBT, and SVM, were evaluated. Among them, the LSTM model achieved the best predictive performance, with R2 of 0.907 and RMSE of 9.65, indicating its strong ability to capture complex nonlinear interactions between process variables and coating responses. The superior performance of LSTM was attributed to its capability to model sequential dependencies and nonlinear coupling effects in limited datasets.
To realize multi-objective optimization, a genetic algorithm (GA) was coupled with the trained LSTM model, enabling simultaneous maximization of microhardness and minimization of corrosion current density. Three representative parameter sets were obtained to prioritize hardness, corrosion resistance, and balanced performance, respectively. The experimentally validated prediction errors for both properties ranged from 2.3% to 5.5%, confirming the reliability of the proposed ML-driven optimization strategy. Microstructural characterization revealed that the optimized coatings consisted primarily of γ-(Co, Fe) solid solution and Cr7C3 and Cr23C6, with a refined grain structure and homogeneous carbide distribution. The evolution of microstructure was governed by laser energy density, which controlled melt pool temperature, lifetime, and Marangoni convection. Low heat input conditions promoted rapid solidification and high nucleation rates, resulting in fine equiaxed grains and dispersed carbides, thereby enhancing hardness through grain boundary strengthening and dispersion hardening. In contrast, high heat input increased dilution and prolonged solidification time, leading to grain coarsening and partial dissolution of carbides, which reduced hardness but improved corrosion resistance by forming a denser and more homogeneous passive film.
A clear trade-off mechanism between hardness and corrosion resistance was identified: reduced laser energy input favored mechanical strengthening, while increased energy input enhanced metallurgical bonding and passivation behavior. The optimized coating exhibited a microhardness of 601HV0.2 and a corrosion current density as low as 2.439×10-6 A/cm2, demonstrating a significant improvement compared with the substrate. Wear tests further confirmed that the optimized coating showed reduced friction coefficient and shallower wear tracks, with the dominant wear mechanism transitioning from severe abrasive wear in the substrate to mild adhesive-abrasive wear in the coating.
This work provides a systematic methodology for integrating machine learning with laser processing to achieve intelligent process design. The proposed framework not only improves prediction accuracy under limited data conditions but also enables efficient identification of optimal process windows for multi-performance targets. The findings highlight the critical role of energy density in governing microstructure and property relationships and offer a generalizable strategy for data-driven optimization of surface engineering systems.
关键词
激光熔覆 /
机器学习 /
显微硬度 /
电化学腐蚀 /
钴基合金
Key words
laser cladding /
machine learning /
microhardness /
electrochemical corrosion /
cobalt-based alloy
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基金
无锡市科技发展资金“太湖之光”科技攻关（基础研究）（K20221051）
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