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Development of a comprehensive model for predicting melt pool characteristics with dissimilar materials in selective laser melting processes
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Development of a comprehensive model for predicting melt pool characteristics with dissimilar materials in selective laser melting processes

Nitesh Anand, Kai-Chun Chang, An-Chou Yeh, Yu-Bin ChenMing-Tsang Lee
Journal of Materials Processing Technology, 卷.319, 118069
10/2023

摘要

Marangoni force Melt pool Normalized enthalpy Recoil pressure Selective laser melting Ceramics and Composites Computer Science Applications Metals and Alloys Industrial and Manufacturing Engineering
Selective laser melting (SLM) is a popular additive manufacturing (AM) technique for the fabrication of precise components. The success of SLM relies on accurate control of the melt pool, where the metal powder melts and solidifies. Fluid flow inside the melt pool is driven by forces like Marangoni force and recoil pressure. SLM process begins with the fabrication of support, which involves the process of joining dissimilar metals. A strong support-base substrate connection ensures the success of subsequent additive manufacturing. In this study a comprehensive model was developed to predict the dimensions and characteristics of the melt pool with a focus on the first layer of the SLM process. An initial numerical simulation was done to obtain transient melt pool temperature and velocity distribution during the SLM process. The results were then coupled with a theoretical analysis development of the proposed model. The effectiveness of the model was validated by experimental results and melt pool characteristics from previous publications. The model shows Root Mean Square Error (RMSE) of 0.53 and 0.49 for height-to-width ratio and normalized area, respectively. The Mean Absolute Percentage Error (MAPE) of normalized area is 15.7 % for the majority of the data, which demonstrates a good accuracy of the model. In addition, the transition from conduction mode to keyhole mode can be identified with the model. This model can be used to rapidly and effectively predict the melt pool characteristics for SLM and other similar AM processes with a wide range of processing parameters.

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