Session: CIE-03-01 Artificial Intelligence and Machine Learning in Design and Manufacturing
Paper Number: 71266
Start Time: August 17, 10:00 AM
71266 - Hybrid Modeling of Melt Pool Geometry in Additive Manufacturing Using Neural Networks
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. One key indicator for the potential performance of a fabricated part is the geometry 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. 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 building process of various scanning strategies.
Presenting Author: Zhuo Yang University of Massachusetts
Authors:
Kevontrez Jones Northwestern UniversityZhuo Yang University of Massachusetts
Ho Yeung National Institute of Standards and Technology (NIST)
Paul Witherell National Institute of Standards and Technology (NIST)
Yan Lu National Institute of Standards and Technology (NIST)
Hybrid Modeling of Melt Pool Geometry in Additive Manufacturing Using Neural Networks
Paper Type
Technical Paper Publication