激光熔覆层工艺几何特征GWO-BPNN算法预测

董刚; 蒋智月; 王敏捷; 范少君; 范东盛; 姚喆赫; 陈智君; 张群莉

doi:10.16490/j.cnki.issn.1001-3660.2026.08.006

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表面技术 ›› 2026, Vol. 55 ›› Issue (8) : 67-82. DOI: 10.16490/j.cnki.issn.1001-3660.2026.08.006

激光表面改性技术

激光熔覆层工艺几何特征GWO-BPNN算法预测

董刚^1a,1b,1c,*, 蒋智月^1a,1b,1c, 王敏捷^1a,1b,1c, 范少君^1a,1b,1c, 范东盛^1a,1c,2, 姚喆赫^1a,1b,1c, 陈智君^1a,1b,1c, 张群莉^1a,1b,1c

作者信息 +

Prediction of Geometric Characteristics of Laser Cladding Process by the GWO-BPNN Algorithm

DONG Gang^1a,1b,1c,*, JIANG Zhiyue^1a,1b,1c, WANG Minjie^1a,1b,1c, FAN Shaojun^1a,1b,1c, FAN Dongsheng^1a,1c,2, YAO Zhehe^1a,1b,1c, CHEN Zhijun^1a,1b,1c, ZHANG Qunli^1a,1b,1c

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文章历史 +

摘要

目的激光熔覆表面几何特征直接影响到材料性能,为节省成本与时间,通过预测及优化激光熔覆层的各项几何特征(稀释率、成形系数及润湿角),采用对应算法对这些几何特征进行优化。方法通过以多项工艺参数建立的多项式回归模型预测熔覆层熔宽熔高,采用灰狼优化算法与BP神经网络结合算法模型（GWO-BPNN算法）预测其成形系数、润湿角、稀释率,最终通过在316L合金不锈钢上熔覆316L合金粉末进行激光工艺全因子实验验证与模型逆向验证。结果基于GWO-BPNN算法的预测模型,在熔覆层稀释率、成形系数及润湿角等关键质量指标上表现出优异的预测能力,其平均决定系数R²达到95.02%,较传统算法（BPNN）平均决定系数R²82.93%显著提升12.4%,构建的回归模型预测三维形貌重构相对误差控制在4.2%以内,验证了算法系统在工艺参数动态预测及优化过程中的实用性。实验验证与逆向验证均证实,GWO-BPNN算法对激光熔覆层各项几何特征（稀释率、成形系数、润湿角）的预测与优化具有良好的准确性。结论 GWO-BPNN算法预测整体预测趋势满足工程容差要求,可以为熔覆层质量的多维度优化提供量化依据,验证了该算法预测在工业场景中的实用性与科学性。

Abstract

Laser cladding, as an advanced green surface modification technology, has been widely applied in aerospace and other high-end fields due to its prominent advantages such as rapid alloy coating fabrication, cost-effectiveness, and high forming efficiency. The morphology of the cladding layer, a crucial characteristic indicator, encompasses key geometric parameters including width, height, depth of fusion, and contact angle, which directly determines the final forming quality and service performance of the components. However, traditional process optimization methods like single-variable analysis and orthogonal experiments suffer from significant efficiency bottlenecks. The trial-and-error parameter optimization process not only consumes substantial material and energy resources but also prolongs the research and development cycle. To address this challenge, establishing an accurate mathematical model for the cross-sectional morphology of single-track cladding has become the theoretical foundation for precise process control. Machine learning-based parameter optimization strategies which construct nonlinear mapping relationships between process parameters and forming quality, offer a promising way to break through the limitations of traditional methods, remarkably improving R&D efficiency and reducing experimental costs. Nevertheless, existing machine learning methods struggle to ensure optimal solutions for multiple objectives simultaneously in multi-dimensional optimization scenarios. In addition, the existing prediction models mostly focus on a single process parameter and fail to achieve comprehensive optimization of multi-dimensional parameters. Grey Wolf Optimizer (GWO), a robust multi-objective optimization algorithm inspired by the hunting behavior of grey wolves, excels in balancing global exploration and local exploitation, making it effective in overcoming this dilemma.
In this study, a polynomial regression model was established based on multiple process parameters to predict the width and height of the cladding layer. Meanwhile, a hybrid algorithm model combining GWO and Back Propagation Neural Network (GWO-BPNN) was proposed to forecast critical quality indicators including forming coefficient, contact angle, and dilution rate. Full-factor laser cladding experiments were conducted by depositing 316L alloy powder on 316L stainless steel substrates, followed by experimental validation and model inverse verification. The results demonstrated that the GWO-BPNN prediction model exhibited excellent performance in predicting key quality indicators. The average coefficient of determination (R²) reached 95.02%, a significant improvement of 12.4% compared with that of the traditional BPNN algorithm (R² = 82.93%). Additionally, the relative error of 3D morphology reconstruction predicted by the regression model was controlled within 4.2%, verifying the practicality of the algorithm system in dynamic process parameter prediction and optimization. Both experimental and inverse verification confirmed the accuracy of the GWO-BPNN algorithm in predicting and optimizing various geometric characteristics of laser cladding layers. In conclusion, the overall prediction trend of the GWO-BPNN algorithm meets engineering tolerance requirements, providing a quantitative basis for the multi-dimensional optimization of cladding layer quality and validating the practicality and scientificity of the proposed evaluation indicators in industrial scenarios. This study is expected to effectively reduce test costs, improve production process stability and finished product quality, and provide new solutions for the intelligent development of laser cladding technology and also provides a theoretical basis and engineering practice basis for the intelligent upgrading of laser cladding process exploration.

导出引用

董刚, 蒋智月, 王敏捷, 范少君, 范东盛, 姚喆赫, 陈智君, 张群莉. 激光熔覆层工艺几何特征GWO-BPNN算法预测[J]. 表面技术. 2026, 55(8): 67-82

DONG Gang, JIANG Zhiyue, WANG Minjie, FAN Shaojun, FAN Dongsheng, YAO Zhehe, CHEN Zhijun, ZHANG Qunli. Prediction of Geometric Characteristics of Laser Cladding Process by the GWO-BPNN Algorithm[J]. Surface Technology. 2026, 55(8): 67-82

中图分类号： TG174.4

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