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HYBRID MODELING OF MELT POOL GEOMETRY IN ADDITIVE MANUFACTURING USING NEURAL NETWORKS

Published

Author(s)

Kevontrez Jones, Zhuo Yang, Ho Yeung, Paul Witherell, Yan Lu

Abstract

Laser powder-bed fusion is an additive manufacturing (AM) process that offers exciting advantages for the fabrication of metallic parts compared to traditional techniques, such as the ability to create complex geometries with less material waste. However, the intricacy of the additive process and extreme cyclic heating and cooling leads to material defects and variations in mechanical properties; this often results in unpredictable and even inferior performance of additively manufactured materials. Key indicators for the potential performance of a fabricated part are the geometry and temperature of the melt pool during the building process, due to its impact upon the underlining microstructure. Computational models, such as those based on the finite element method, of the AM process can be used to elucidate and predict the effects of various process parameters on the melt pool, according to physical principles. However, these physics-based models tend to be too computationally expensive for real-time process control. Hence, in this work, a hybrid model utilizing neural networks is proposed and demonstrated to be an accurate and efficient alternative for predicting melt pool geometries in AM, which provides a unified picture of melting conditions. The results of both a physics-based finite element model and the hybrid model are compared to real-time experimental measurements of the melt pool during the single-layer building process of various scanning strategies.
Proceedings Title
Proceedings of the ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
Conference Dates
August 15-19, 2021
Conference Location
Vitual, MD, US
Conference Title
The ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference

Keywords

Additive Manufacturing, Neural Networks, Melt Pool Dimension Prediction, Finite Element Method

Citation

Jones, K. , Yang, Z. , Yeung, H. , Witherell, P. and Lu, Y. (2021), HYBRID MODELING OF MELT POOL GEOMETRY IN ADDITIVE MANUFACTURING USING NEURAL NETWORKS, Proceedings of the ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Vitual, MD, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932570 (Accessed April 23, 2024)
Created November 17, 2021, Updated November 29, 2022