Abstract
A numerical analysis model that can accurately predict the physical characteristics of the actually additive manufactured products can significantly reduce time and costs for experimental builds and tests. Thermal analysis for the metal AM process simulation requires a lot of analysis parameters and conditions. However, their accuracy and reliability are not clear, and the current understanding of their influence on the analysis results is very insufficient. Therefore, in this study, the influence of uncertain analysis parameters on the thermal analysis results is estimated, and a procedure to calibrate these analysis parameters is proposed. By using the thermal analysis results for parameter cases determined by a design of experiments, a regression analysis model is constructed to estimate the sensitivity of the analysis parameters to the thermal analysis results. Additionally, it is used to determine the optimal values of analysis parameters that can produce the thermal analysis results closest to the given reference data from actual builds. By using the melt pool size computed from a numerical model as reference data, the proposed procedure is validated. From this result, it is confirmed that a high-fidelity thermal analysis model that can predict the characteristics of actual builds from minimal experimental builds can be constructed efficiently.
Highlights
Additive manufacturing (AM) is a manufacturing technology that builds a 3D structure from a digital model with a layer-by-layer sequence, and research to apply AM technology to the production of parts is being actively conducted worldwide
A new technique to improve the accuracy of the thermal analysis model for Laser Powder Bed Fusion (LPBF) process simulation by nonlinear regression and an optimization algorithm was proposed, and validation was performed by evaluating the errors using a finite element thermal analysis model
A regression model was constructed using the thermal analysis results for analysis parameter cases generated by Box–Behnken design, and sensitivity analysis and parameter optimization were performed on analysis parameters
Summary
Additive manufacturing (AM) is a manufacturing technology that builds a 3D structure from a digital model with a layer-by-layer sequence, and research to apply AM technology to the production of parts is being actively conducted worldwide. Dong et al [7] analyzed the effect of hatching spacing on temperature field, microstructure and melt pool size, overlap rate, surface quality, and relative density by experiments and simulations, and they determined the optimal hatch spacing In these studies related to AM process simulations using FEM, several assumptions were made to determine the analysis parameters and conditions. In addition to methods using FEM, many studies have been conducted using CFD and analytical models to propose accurate thermal analysis models for LPBF process simulation and prediction of the melt pool size. Rubenchik et al [16] determined that the temperature distribution in the simple thermal model of SLM was characterized by two dimensionless parameters (normalized enthalpy and the ratio of dwell time to the diffusion time) In these studies that predict the melt pool size using CFD and analytical models, analysis parameters have very significant effects on the analysis results, while there are many analysis parameters with high uncertainty.
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