研究目的
To develop an efficient and accurate finite element model for predicting transient material thermal behavior during the selective laser melting (SLM) process in part-level, which can be used to optimize process parameters and can be extended to other additive manufacturing processes.
研究成果
The developed FE model provides an efficient and accurate tool for simulating part-level temperature fields in the SLM process, with simulation speeds 12× to 18× faster than traditional schemes. The model can predict thermal cycles comparable to experimental results and can be used to optimize process parameters and extend to other additive manufacturing processes.
研究不足
The model neglects the Marangoni effect and heat loss due to vaporization inside the melt pool, which may lead to overestimation of peak temperatures and underestimation of cooling rates. The simulation is also limited by the computational cost, although it is significantly reduced compared to traditional methods.
1：Experimental Design and Method Selection:
The research utilizes an open-source finite element library (Deal.II) to develop a simulation scheme that adaptively refines and coarsens the mesh and solves finite element equations in parallel. A new mesh strategy is developed to reduce the element number while maintaining solution accuracy.
2：Sample Selection and Data Sources:
The simulation focuses on the SLM process of Inconel 718, with temperature fields and material properties derived from experimental data and literature.
3：List of Experimental Equipment and Materials:
The simulation uses temperature-dependent material properties for Inconel 718, including thermal conductivity, specific heat, and enthalpy. Process parameters such as laser power, scanning speed, and layer thickness are specified.
4：Experimental Procedures and Operational Workflow:
The simulation process is driven by a scanning path file, which includes process parameters for each track and layer. The simulation proceeds track-by-track and layer-by-layer, with adaptive mesh refinement based on the current scanning track.
5：Data Analysis Methods:
The simulation results are compared with experimental temperature history data to validate the model. The temperature distribution and material phase transitions are analyzed to understand the thermal behavior during the SLM process.
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