Numerical Simulation and Process Parameter Optimization of Laser Hardening for QT500-7 Ductile Cast Iron

LIANG Qiang; CHEN Hong; XU Binyuan; ZHAO Bin; JIA Yanyan

doi:10.16490/j.cnki.issn.1001-3660.2026.08.010

PDF(9982 KB)

Surface Technology ›› 2026, Vol. 55 ›› Issue (8) : 122-137. DOI: 10.16490/j.cnki.issn.1001-3660.2026.08.010

Laser Surface Modification Technology

Numerical Simulation and Process Parameter Optimization of Laser Hardening for QT500-7 Ductile Cast Iron

LIANG Qiang^a,b,*, CHEN Hong^a, XU Binyuan^a, ZHAO Bin^a,b, JIA Yanyan^a,b

Author information +

History +

Abstract

To enhance the laser hardening effect on the surface of QT500-7 ductile cast iron, the work aims to propose a novel hybrid model, BO-RF-XGBOOST, to predict the optimal combination of laser hardening process parameters. Firstly, the numerical ranges of laser power (P), scanning speed (V), and overlap rate (f) were preliminarily selected with a finite element model of phase transformation and heat transfer. A three-factor, three-level experiment was designed, with hardened layer depth and fused layer depth as response targets. Based on the experimental data, four regression prediction models were established for optimizing laser surface hardening parameters of QT500-7 ductile cast iron to achieve superior surface mechanical properties. A comprehensive framework integrating computational modeling, machine learning techniques, and multi-objective optimization algorithms was developed to systematically determine the optimal laser processing conditions. A sophisticated finite element model incorporating phase transformation kinetics and heat transfer mechanisms was firstly established to simulate the laser hardening process, providing fundamental understanding of the thermal interactions. Based on numerical simulations, the critical process parameters including P (100-400 W), V (5-15 mm·s^-1), and f (60%-90%) were identified, forming the basis for a carefully designed three-factor, three-level experimental matrix with fused layer depth and hardened layer depth as primary quality indicators. The experimental data enabled the development and rigorous comparison of four machine learning architectures: a baseline Random Forest (RF) model, an Extreme Gradient Boosting (XGBOOST) model, an RF-XGBOOST hybrid ensemble, and a Bayesian Optimization (BO)-enhanced RF-XGBOOST (BO-RF-XGBOOST) model. Extensive evaluation demonstrated the superior predictive capability of the BO-XGBOOST-RF hybrid model, achieving remarkable accuracy with prediction errors of merely 6.52% for hardened layer depth and 9.09% for fused layer depth. At the optimization phase, three advanced algorithms of Advantage Actor-Critic (A2C), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) were systematically compared, with AC2 emerging as the most effective approach in terms of solution quality and computational efficiency. The optimized parameters (P of 230 W, V of 14 mm·s^-1, f of 75%) were experimentally validated, producing a hardened layer depth of 230 μm and fused layer depth of 66 μm, both closely matching model predictions. Microhardness characterization revealed exceptional surface property enhancement, with hardness values increasing from the base material's (166±15)HV0.5 to (940±40)HV0.5 in the fused zone and (630±30)HV0.5 in the hardened zone, representing 5.7-fold and 3.8-fold improvements respectively. The developed methodology provides significant theoretical and practical contributions for laser surface engineering of cast irons. The BO-RF-XGBOOST hybrid model establishes a new benchmark for process parameter prediction accuracy, while the integrated A2C/TOPSIS-EWM optimization framework offers a robust approach for multi-criteria decision making in manufacturing processes. Metallurgical analysis confirms the formation of refined martensitic microstructures in the treated regions, explaining the substantial mechanical property enhancement while maintaining excellent surface integrity without cracks or defects. The experimental results obtained in this work demonstrate the remarkable reliability of the implemented comprehensive algorithm. This modular design methodology presents a viable and superior alternative to conventional parameter optimization approaches that rely solely on physical experimentation. The proposed framework establishes a valuable reference paradigm that can be effectively extended to surface modification and strengthening treatments for various other cast iron materials.

Key words

QT500-7 ductile cast iron / finite element model / laser hardening / BO-RF-XGBOOST algorithm / multi- objective optimization

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LIANG Qiang, CHEN Hong, XU Binyuan, ZHAO Bin, JIA Yanyan. Numerical Simulation and Process Parameter Optimization of Laser Hardening for QT500-7 Ductile Cast Iron[J]. Surface Technology. 2026, 55(8): 122-137

References

[1] 王泽华, 徐飞龙, 张欣, 等. 关于球墨铸铁球化率评定方法的初步探讨[J]. 铸造, 2017, 66(4): 348-354.
WANG Z H, XU F L, ZHANG X, et al.Discussion on Test Methods for Evaluating the Ductile Iron[J]. Foundry, 2017, 66(4): 348-354.
[2] 郑国华, 张欣耀, 陈沛, 等. 球墨铸铁断裂韧度测试技术研究进展[J]. 机械工程材料, 2021, 45(10): 22-28.
ZHENG G H, ZHANG X Y, CHEN P, et al.Research Progress on Fracture Toughness Testing Technology of Ductile Cast Iron[J]. Materials for Mechanical Engineering, 2021, 45(10): 22-28.
[3] 洪妙, 刘佳, 石岩, 等. 工艺参数对球墨铸铁和低碳钢激光焊接的影响[J]. 激光技术, 2024, 48(1): 54-59.
HONG M, LIU J, SHI Y, et al.Effect of Process Parameters on Laser Welding of Nodular Cast Iron and Low Carbon Steel[J]. Laser Technology, 2024, 48(1): 54-59.
[4] 路世盛, 周健松, 王凌倩, 等. 球墨铸铁表面激光熔覆Ni-Co复合涂层的耐腐蚀及高温摩擦学性能[J]. 中国表面工程, 2022, 35(3): 122-131.
LU S S, ZHOU J S, WANG L Q, et al.Corrosion Resistance and Elevated-Temperature Wear Properties of Laser Cladding Ni-Co Composite Coating on Ductile Cast Iron[J]. China Surface Engineering, 2022, 35(3): 122-131.
[5] 党钰钦, 李冬杰, 刘艳梅, 等. 球墨铸铁表面激光熔覆层的组织及耐腐蚀性能研究[J]. 表面技术, 2024, 53(17): 126-134.
DANG Y Q, LI D J, LIU Y M, et al.Microstructure and Corrosion Resistance of the Laser Cladding Layer on Nodular Cast Iron Surface[J]. Surface Technology, 2024, 53(17): 126-134.
[6] 童文辉, 赵子龙, 张新元, 等. 球墨铸铁表面激光熔覆TiC/钴基合金组织和性能研究[J]. 金属学报, 2017, 53(4): 472-478.
TONG W H, ZHAO Z L, ZHANG X Y, et al.Microstructure and Properties of TiC/Co-Based Alloy by Laser Cladding on the Surface of Nodular Graphite Cast Iron[J]. Acta Metallurgica Sinica, 2017, 53(4): 472-478.
[7] JACQUES L, STEVE D, ALAIN H.Cast Iron: A Historical and Green Material Worthy of Continuous Research[J]. International Journal of Technology, 2021, 12(6): 1123.
[8] 王旭, 虞钢, 何秀丽, 等. 扫描速度对CuCr合金激光表面快速熔凝改性层性能的影响[J]. 激光与光电子学进展, 2022, 59(1): 0114006.
WANG X, YU G, HE X L, et al.Effect of Scanning Speed on Properties of Laser Surface Remelting Layer of CuCr Alloy[J]. Laser & Optoelectronics Progress, 2022, 59(1): 0114006.
[9] 秦茶, 张琰, 赵清华. 激光强化合金球墨铸铁轧辊的微观组织及性能[J]. 金属热处理, 2011, 36(6): 21-24.
QIN C, ZHANG Y, ZHAO Q H.Microstructure and Properties of Alloy Nodular Cast Iron Roller Treated by Laser Hardening[J]. Heat Treatment of Metals, 2011, 36(6): 21-24.
[10] BABU P D, MARIMUTHU P.Status of Laser Transformation Hardening of Steel and Its Alloys: A Review[J]. Emerging Materials Research, 2019, 8(2): 188-205.
[11] 周显敏, 曾大新, 杨伟, 等. 球墨铸铁与灰口铸铁激光表面硬化能力对比[J]. 金属热处理, 2024, 49(12): 229-236.
ZHOU X M, ZENG D X, YANG W, et al.Comparison of Laser Surface Hardening Ability between Nodular Cast Iron and Gray Cast Iron[J]. Heat Treatment of Metals, 2024, 49(12): 229-236.
[12] 梁荣, 常晓惠, 谢伟, 等. 压射头的激光表面硬化工艺[J]. 金属热处理, 2014, 39(5): 101-103.
LIANG R, CHANG X H, XIE W, et al.Surface Laser Hardening of Punch Head[J]. Heat Treatment of Metals, 2014, 39(5): 101-103.
[13] 唐亮, 王文健, 张亚龙, 等. 激光淬火工艺对QT700-2球墨铸铁表面硬度与硬化层深度的影响[J]. 机械工程材料, 2020, 44(5): 82-86.
TANG L, WANG W J, ZHANG Y L, et al.Effect of Laser Quenching Process on Surface Hardness and Hardened Layer Depth of QT700-2 Ductile Cast Iron[J]. Materials for Mechanical Engineering, 2020, 44(5): 82-86.
[14] 樊湘芳, 何彬, 罗玉梅. 球墨铸铁的激光相变硬化[J]. 金属热处理, 2006, 31(11): 29-31.
FAN X F, HE B, LUO Y M.Laser Hardening of Nodular Cast Iron[J]. Heat Treatment of Metals, 2006, 31(11): 29-31.
[15] FAKIR R, BARKA N, BROUSSEAU J.Case Study of Laser Hardening Process Applied to 4340 Steel Cylindrical Specimens Using Simulation and Experimental Validation[J]. Case Studies in Thermal Engineering, 2018, 11: 15-25.
[16] FORTUNATO A, ASCARI A, ORAZI L, et al.Numerical Evaluation of the Reflectivity Coefficient in Laser Surface Hardening Simulation[J]. Surface and Coatings Technology, 2012, 206(14): 3179-3185.
[17] SIDI-AHMED K, MAOUCHE B, GABI Y, et al.Numerical Simulations and Experimental Investigation of Laser Hardening Depth Investigation via 3MA-Eddy Current Technique[J]. Journal of Magnetism and Magnetic Materials, 2022, 550: 169046.
[18] CHEN Z Y, YU X D, DING N, et al.Wear Resistance Enhancement of QT700-2 Ductile Iron Crankshaft Processed by Laser Hardening[J]. Optics & Laser Technology, 2023, 164: 109519.
[19] DJURDJEVIC M, JOVANOVIC V, STOPIC S.Quantifying Latent Heat in AlSi₅Cu Alloys (with 1, 2, and 4% of Cu by Mass) via DSC, Thermal Analysis, and Commercial Software[J]. Metals, 2025, 15(9): 1045.
[20] 林振铭, 唐嘉希, 何雪松, 等. 球墨铸铁注塑机头板的铸造工艺设计[J]. 现代铸铁, 2015, 35(2): 19-22.
LIN Z M, TANG J X, HE X S, et al.Casting Method Design of Head Plate Used for Plastic Injecting Machine[J]. Modern Cast Iron, 2015, 35(2): 19-22.
[21] MOHAJERANI S, MILLER J D, TUTUNEA-FATAN O R, et al. Thermo-Physical Modelling of Track Width during Laser Polishing of H13 Tool Steel[J]. Procedia Manufacturing, 2017, 10: 708-719.
[22] 李昌, 邓双九, 高鹤芯, 等. 考虑相变诱导塑性下40Cr激光淬火工艺参数显著性分析[J]. 航空动力学报, 2024, 39(4): 84-98.
LI C, DENG S J, GAO H X, et al.Significance of 40Cr Laser Quenching Process Parameters Considering Transformation Induced Plasticity[J]. Journal of Aerospace Power, 2024, 39(4): 84-98.
[23] LEBLOND J B, DEVAUX J, DEVAUX J C.Mathematical Modelling of Transformation Plasticity in Steels I: Case of Ideal-Plastic Phases[J]. International Journal of Plasticity, 1989, 5(6): 551-572.
[24] 郭怡晖, 刘继常, 卢远志, 等. 球墨铸铁QT600-3激光相变硬化数值模拟研究[J]. 强激光与粒子束, 2010, 22(8): 1755.
GUO Y H, LIU J C, LU Y Z, et al.Numerical Simulation of Laser Transformation Hardening of Ductile Cast Iron QT600-3[J]. High Power Laser and Particle Beams, 2010, 22(8): 1755.
[25] 许彦, 李昌, 贾腾辉, 等. QT600球墨铸铁激光熔覆数值模拟与实验研究[J]. 表面技术, 2022, 51(7): 377-387.
XU Y, LI C, JIA T H, et al.Numerical Simulation and Experimental Research on the Laser Cladding Process of QT600 Nodular Iron[J]. Surface Technology, 2022, 51(7): 377-387.
[26] 刘继常, 罗旦, 许阳辉. 球墨铸铁激光相变硬化深度与宽度的数值计算[J]. 材料热处理学报, 2013, 34(12): 183-187.
LIU J C, LUO D, XU Y H.Numerical Calculation of Depth and Width of Laser-Hardened Band for Nodular Cast Iron[J]. Transactions of Materials and Heat Treatment, 2013, 34(12): 183-187.
[27] TEMMLER A, LIU D, PREUßNER J, et al. Influence of Laser Polishing on Surface Roughness and Microstructural Properties of the Remelted Surface Boundary Layer of Tool Steel H11[J]. Materials & Design, 2020, 192: 108689.
[28] ZHAO Z, CHEN W L, XIE X C, et al.Wear Resistance of Cronidur 30 Steel Enhanced by Optimizing the Strengthened Grinding Process (SGP) Parameters Using a Box-Behnken Design (BBD) Method[J]. Journal of Manufacturing Processes, 2024, 122: 7-20.
[29] BREIMAN L.Random Forests[J]. Machine Learning, 2001, 45(1): 5-32.
[30] CHEN T Q, GUESTRIN C.XGBoost: A Scalable Tree Boosting System[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA. ACM, 2016: 785-794.
[31] 赵希坤, 李聪波, 杨勇, 等. 数据-机理混合驱动下考虑刀具柔性的柔性加工工艺参数能效优化方法[J]. 机械工程学报, 2024, 60(7): 236-248.
ZHAO X K, LI C B, YANG Y, et al.A Data and Model Hybrid Driven Cutting Parameter Energy-Efficiency Optimization Method for Flexible Machining Process Considering Cutting Tool Flexibility[J]. Journal of Mechanical Engineering, 2024, 60(7): 236-248.
[32] ZHENG Y, LI X T, XU L.Balance Control for the First-Order Inverted Pendulum Based on the Advantage Actor-Critic Algorithm[J]. International Journal of Control, Automation and Systems, 2020, 18(12): 3093-3100.
[33] LV J S, SUN Y W, ZHANG Z Q, et al.Optimization of Operational Parameters of Marine Methanol Dual-Fuel Engine Based on RSM-MOPSO[J]. Process Safety and Environmental Protection, 2024, 191: 2634-2652.
[34] DEB K, PRATAP A, AGARWAL S, et al.A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[35] 蒋荣超, 刘大维, 王登峰. 基于熵权TOPSIS方法的整车动力学性能多目标优化[J]. 机械工程学报, 2018, 54(2): 150-158.
JIANG R C, LIU D W, WANG D F.Multi-Objective Optimization of Vehicle Dynamics Performance Based on Entropy Weighted TOPSIS Method[J]. Journal of Mechanical Engineering, 2018, 54(2): 150-158.
[36] WANG D F, JIANG R C, WU Y C.A Hybrid Method of Modified NSGA-II and TOPSIS for Lightweight Design of Parameterized Passenger Car Sub-Frame[J]. Journal of Mechanical Science and Technology, 2016, 30(11): 4909-4917.

Funding

The General project of Chongqing Natural Science Foundation (CSTB2025NSCQ-GPX0135); The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K202500802)