激光粉末床熔融模具钢 H13 热力仿真验证及硬度调控分析 (特邀)

Objective Laser powder bed fusion (LPBF) has been widely applied in the manufacturing of die steels due to its ability to produce complex geometries with high precision. However, the LPBF processing of H13 tool steel presents several challenges, including unstable melt pool dynamics, rapid thermal fluctuations, and difficulties in controlling microstructure and mechanical properties. These issues compromise the consistency and mechanical reliability of fabricated components. While most existing studies focus on single physical fields-such as the temperature field-they often neglect the coupled effects of thermal and mechanical evolution. To address this limitation, this study proposes a coupled thermal-mechanical simulation framework that integrates a powder-solid dual-state material model, temperature-dependent material properties, and dynamic latent heat treatment. The model is validated experimentally and employed to investigate the influence of scanning parameters on melt pool temperature, residual stress, and hardness. The overall objective is to establish a systematic understanding of how LPBF process parameters affect the hardness of H13 tool steel. Methods A finite element simulation model was developed incorporating a powder-solid dual-state material framework, a 30 µm powder layer, and a dynamic latent heat phase transformation mechanism, enabling high-fidelity coupling of thermal and mechanical behaviors. A Gaussian volumetric heat source was implemented along an “S” -shaped bidirectional scanning path. A sequential coupling strategy was used to simulate the evolution of three-dimensional temperature and stress fields under varying process parameters, specifically scanning speeds ranging from 500 mm/s to 800 mm/s and hatch spacings from 60 µm to 100 µm. Experimentally, gas-atomized H13 powder was used to fabricate 25 sets of cubic samples via LPBF under the same parameter ranges. Microstructural characterization was performed using optical microscopy and scanning electron microscopy, while Brinell hardness tests were conducted to evaluate mechanical performance. The experimental results were systematically compared with simulation outputs to validate the accuracy and applicability of the proposed model. Results and Discussions Simulation results reveal a significant interaction between scanning speed and hatch spacing in governing melt pool thermal accumulation, cooling rate, and stress concentration. At a scanning speed of 800 mm/s and a hatch spacing of 60 µm, the peak melt pool temperature remains moderate, while the cooling rate is substantially increased. This parameter combination reduces the maximum residual stress to 176 MPa, with both the Von Mises stress and the principal stress in the x-direction exhibiting smoother and more uniform distributions compared to other conditions (Figs. 16-17). Experimental validation shows that samples produced under these conditions achieve a Brinell hardness of HB 580 ± 12, representing approximately 9.8% improvement over the baseline (Fig.21). The microstructure consists of fine, dense martensite with a mixed columnar and cellular morphology (Figs.19-20), which aligns closely with the predicted thermal field behavior. Conclusions This study develops a high-fidelity multi-physics simulation framework coupled with experimental validation for LPBF fabrication of H13 tool steel. The proposed approach systematically elucidates the multi-scale coupling mechanisms between process parameters and the evolution of temperature fields, stress distribution, and mechanical properties. Results confirm that increasing scanning speed and optimizing hatch spacing effectively reduce peak residual stress, enhance cooling rates, and improve hardness. The dual-state powder-solid modeling strategy and dynamic latent heat treatment mechanism demonstrate strong applicability and can be extended to the additive manufacturing of other tool steels and high-performance alloys. This integrated framework provides a robust theoretical foundation and technical support for process visualization, microstructure-property control, and intelligent path optimization in advanced metal additive manufacturing.