| LIANG Qiang,XU Binyuan,XU Yonghang,DU Yanbin,LI Yongliang.Optimization of Laser Hardening Process Parameters for Surface of H13 Steel Based on OOA-RF[J],54(5):217-232, 275 |
| Optimization of Laser Hardening Process Parameters for Surface of H13 Steel Based on OOA-RF |
| Received:June 05, 2024 Revised:July 30, 2024 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2025.05.017 |
| KeyWord:laser surface hardening random forest algorithm finite element model average hardening depth central composite test multi-objective genetic algorithm |
| Author | Institution |
| LIANG Qiang |
School of Mechanic Engineering,Chongqing , China ;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
| XU Binyuan |
School of Mechanic Engineering,Chongqing , China |
| XU Yonghang |
School of Mechanic Engineering,Chongqing , China |
| DU Yanbin |
School of Mechanic Engineering,Chongqing , China ;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
| LI Yongliang |
School of Mechanic Engineering,Chongqing , China ;Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment, Chongqing Technology and Business University, Chongqing , China |
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| Abstract: |
| To enhance the hardening effect of laser action on the surface of H13 steel, this study proposed a method for predicting the process parameters of laser surface hardening based on the Osprey Optimization Algorithm (OOA) optimized Random Forest (RF) algorithm. Firstly, a finite element model was established to simulate the temperature field changes on the surface of the workpiece during laser scanning. Experiments were then conducted under the same process parameters, and the maximum quench depth was measured to validate the effectiveness of the model. The results showed a relative error of 11.0% for the maximum quench depth, indicating that the established finite element model accurately reflected the laser surface hardening process and provided support for selection of process parameter ranges in subsequent studies. The finite element model was utilized to determine the process parameter ranges for laser power, scanning speed, and overlap rate. Subsequently, a three-factor, five-level central composite test (CCD) was conducted within these ranges to derive hardened layer parameters through the finite element model. Then, a response surface methodology (RSM) surface hardening layer prediction model, an RF surface hardening layer prediction model, and a surface hardening layer prediction model based on OOA-RF were constructed separately, and the prediction accuracy of the three models was analyzed and compared. The OOA-RF model was found to have a higher goodness of fit (R2) for the response target compared with the RSM and RF models, indicating its better applicability. Additionally, the mean absolute percentage error values of the OOA-RF model were consistently lower than those of the RSM and RF models, further highlighting its higher fitting accuracy for the response target. By using the multi-objective genetic algorithm (NSGA-Ⅱ) to optimize the established OOA-RF model for process parameters, constraints needed to be applied to both the range of process parameters and the response objectives to achieve a good quenching effect. After optimization, the Pareto front solution set was obtained. To select the best solution from the solution set, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Entropy Weight Method (EWM) were combined to re-rank the optimization solution set and obtain the best combination of process parameters. In the verification test conducted at the optimal process parameters with a power of 517 W, a scanning speed of 5 mm/s, and an overlap rate of 48%, a cross section perpendicular to the scanning path was cut. After polishing and etching the cross section, the average hardened depth was observed to be 723.3 μm, with a relative error of 11.15% compared with the predicted value. Additionally, a peak-to-valley difference of 58.75 μm was achieved with a relative error of 3.77% from the predicted value, and hardened surfaces were relatively flat, without visible depressions. The hardness before laser surface hardening was (165.2±9.2)HV0.5. After surface hardening, the hardness increased to (381.4±86.2)HV0.5, indicating an average hardness enhancement of 1.3 times. The elemental content of hardened and non-hardened areas was similar, and the main reason for hardening was the martensitic transformation of the hardened layer. This method demonstrates potential for optimizing laser surface hardening process parameters for alloys. |
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