Key Takeaways & Executive Findings
- •• • Polynomial regression model predicts clad width and height with relative error ≤4.2%, enabling reliable pre-selection of process parameters and reducing trial-and-error costs in industrial cladding operations. • • GWO-BPNN model achieves average R² of 95.28% for dilution rate, forming coefficient, and wetting angle, a 12.4% improvement over conventional BPNN (82.93%), significantly enhancing prediction accuracy for multi-objective quality optimization. • • Full-factorial experiments on 316L stainless steel with 316L powder validate the model's robustness across varying laser power, powder feed rate, and scanning speed, confirming its applicability for dynamic process control. • • Inverse validation demonstrates stable predictive performance within engineering tolerances, providing a quantitative foundation for real-time parameter adjustment and quality assurance in laser cladding production lines.
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Abstract
Laser cladding is a green surface modification technology widely used in aerospace and other high-end fields, but traditional process optimization methods such as single-variable analysis and orthogonal experiments suffer from low efficiency and high cost. The geometric characteristics of the cladding layer—dilution rate, forming coefficient, and wetting angle—directly determine service performance. Existing machine learning models often fail to achieve multi-objective optimization and comprehensive prediction. This study proposes a hybrid algorithm combining Grey Wolf Optimizer (GWO) with Backpropagation Neural Network (BPNN) to predict geometric quality indicators. Full-factorial single-track laser cladding experiments were conducted on 316L stainless steel with 316L alloy powder. A polynomial regression model predicted clad width and height with relative error below 4.2%. The GWO-BPNN model predicted dilution rate, forming coefficient, and wetting angle with an average coefficient of determination (R²) of 95.28%, a 12.4% improvement over traditional BPNN (82.93%). Experimental and inverse validation confirmed stable predictive performance across different parameter ranges, meeting engineering tolerance requirements. The method provides a quantitative basis for multi-dimensional optimization of cladding quality and demonstrates practical applicability in industrial scenarios.
1. Introduction
Laser cladding is an advanced green surface modification technology that offers rapid alloy coating fabrication, cost-effectiveness, and high forming efficiency, making it indispensable in aerospace and other high-end industries. The geometric morphology of the cladding layer—including width, height, depth of fusion, and contact angle—directly dictates the final forming quality and service performance of components. Traditional process optimization methods, such as single-variable analysis and orthogonal experiments, suffer from significant efficiency bottlenecks. The trial-and-error parameter optimization process consumes substantial material and energy resources and prolongs research and development cycles. Establishing an accurate mathematical model for the cross-sectional morphology of single-track cladding is therefore critical for precise process control.
Existing machine learning-based parameter optimization strategies construct nonlinear mappings between process parameters and forming quality, offering a promising way to overcome the limitations of traditional methods. However, these methods struggle to ensure optimal solutions for multiple objectives simultaneously in multi-dimensional optimization scenarios. Most existing prediction models focus on a single process parameter and fail to achieve comprehensive optimization of multi-dimensional parameters. This study addresses these bottlenecks by integrating the Grey Wolf Optimizer (GWO) with a Backpropagation Neural Network (BPNN) to predict dilution rate, forming coefficient, and wetting angle. Full-factorial experiments on 316L stainless steel validate the model, which achieves an average R² of 95.28%, a 12.4% improvement over traditional BPNN, and demonstrates stable predictive performance across different parameter ranges, meeting engineering tolerance requirements.
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DONG Gang, JIANG Zhiyue, WANG Minjie, FAN Shaojun, FAN Dongsheng, YAO Zhehe, CHEN Zhijun, ZHANG Qunli (2026). Prediction of Geometric Characteristics of Laser Cladding Process by the GWO-BPNN Algorithm. Surface Technology (表面技术). https://doi.org/10.16490/j.cnki.issn.1001-3660.2026.08.006
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Frequently Asked Questions
What is the predictive accuracy of the GWO-BPNN model compared to traditional BPNN for laser cladding geometric features?
The GWO-BPNN model achieves an average coefficient of determination (R²) of 95.28% for dilution rate, forming coefficient, and wetting angle, which is a 12.4% improvement over the traditional BPNN model's 82.93%. This higher accuracy ensures more reliable multi-objective optimization and quality prediction in industrial settings.
How does the polynomial regression model perform in predicting clad width and height, and what are its limitations?
The polynomial regression model predicts clad width and height with a relative error controlled within 4.2%, providing a solid foundation for subsequent quality prediction. However, it only addresses macroscopic geometric features and does not capture multi-dimensional quality indicators such as dilution rate, forming coefficient, and wetting angle, which require the GWO-BPNN model for comprehensive optimization.
What experimental validation was conducted to ensure the GWO-BPNN model's practical applicability?
Full-factorial single-track laser cladding experiments were performed on 316L stainless steel using 316L alloy powder, varying laser power, powder feed rate, and scanning speed. Both experimental and inverse validation confirmed that the model maintains stable predictive performance across different parameter ranges, with overall prediction trends meeting engineering tolerance requirements, demonstrating its practicality for process parameter evaluation and quality prediction.
What are the key industrial benefits of using the GWO-BPNN algorithm for laser cladding process optimization?
The GWO-BPNN algorithm reduces trial-and-error costs by accurately predicting geometric quality indicators, enabling quantitative process parameter optimization. With an average R² of 95.28%, it provides a reliable basis for multi-dimensional quality control, potentially reducing material waste, energy consumption, and R&D cycles in industries such as aerospace where laser cladding is critical.
How does the GWO-BPNN model handle multi-objective optimization compared to existing machine learning methods?
Unlike existing machine learning methods that often focus on a single process parameter and fail to achieve comprehensive optimization, the GWO-BPNN model simultaneously predicts dilution rate, forming coefficient, and wetting angle. The Grey Wolf Optimizer enhances the BPNN's ability to find optimal solutions in multi-dimensional scenarios, resulting in a 12.4% improvement in R² over traditional BPNN, thereby enabling effective multi-objective optimization.
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