Predicting temperature field in keyhole-mode selective laser melting with combined heat sources: a rapid model

Predicting the temperature field during selective laser melting (SLM) is crucial for improving the performance of printed parts. However, there is still a lack of an efficient and accurate model for predicting the temperature field of keyhole-mode melting in SLM. Based on the physical phenomena of k...

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Veröffentlicht in:Physica scripta 2023-09, Vol.98 (9), p.95232
Hauptverfasser: Li, Bing, Li, Changyou, Chunlei, Hua
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Chunlei, Hua
description Predicting the temperature field during selective laser melting (SLM) is crucial for improving the performance of printed parts. However, there is still a lack of an efficient and accurate model for predicting the temperature field of keyhole-mode melting in SLM. Based on the physical phenomena of keyhole-mode melting observed in experiments and simulations, this study proposes an analytical model for rapidly predicting the temperature distribution during SLM keyhole-mode melting. The model considers vapor depression in the molten pool and the interaction between the laser and molten pool during keyhole-mode melting. The model was validated using numerical simulations and experimental data. The variation trend of the laser energy distribution and molten pool size with respect to the laser energy density was revealed. As the laser energy density increased, the depth of the molten pool and the vapor depression increased linearly, and the molten pool width increased to a peak and then remained constant. The process parameter window to avoid a lack-of-fusion was also investigated. With a computation time of 15 s and a prediction error of less than 10%, this model is an effective way to simulate SLM processes and guide the optimization of process parameters.
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subjects additive manufacturing
heat conduction
keyhole-mode melting
molten pool
selective laser melting
thermal simulation
title Predicting temperature field in keyhole-mode selective laser melting with combined heat sources: a rapid model
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