|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Using Machine Learning to Improve Melt Pool Prediction in Additive Manufacturing: Data Denoising and Predictive Modeling
||Yaohong Xiao, Zhuo Wang, Lei Chen
|On-Site Speaker (Planned)
Accurate modeling of melt pool is vital to achieving process optimization and quality control in additive manufacturing. In this research, a data-driven melt pool modeling framework based on machine learning (ML) is established, further fueled by massive experimental data from National Institute of Standards and Technology (NIST). First, a convolutional neural network is used to pre-process as-received melt pool images, enabling removal of spattering noises and thus extraction of high-quality melt pool data. Then, a novel melt pool prediction model using multi-layer perceptron is trained by incorporating raw, long scanning history as input features, which best accounts for effects (e.g., heating) of printing history on melt pool development. Testing results under various manufacturing conditions show that the average relative error of predicting melt pool area drops to 2.8 %, compared to 14.8 % of prior art–the Neighboring Effect Modeling Method, representing a significant step towards reliable melt-pool-guided quality control.
||Additive Manufacturing, Modeling and Simulation, Machine Learning