Explainable AI for layer-wise emission prediction in laser fusion

Guo，Weihong1,2; Gawade，Vidita1,2; Zhang，Bi3; Guo，Yuebin2,4

2023

10.1016/j.cirp.2023.03.009

CIRP Annals   Impact Factor & Quartile

0007-8506

1726-0604

The dynamic behavior of melt pools in powder bed-based laser fusion is very challenging to model using physics-based models and conventional black-box data-driven models. Explainable Artificial Intelligence is developed in this work to advance the understanding of convoluted links of non-sequential process physics, online time series sensing data, and process anomaly (e.g., overheating in the melt pool). A Shapley Additive Explanations (SHAP)-enabled Deep Neural Network-Long Short-Term Memory (DNN-LSTM) model has been developed as a mechanism to integrate process parameter knowledge with process history information through online sensing data while providing local and global model interpretation and transparency.

Additive manufacturing Explainable AI Machine learning

English

Others

ENGINEERING

2-s2.0-85151751458

Scopus

1.Department of Industrial and Systems Engineering,Rutgers University-New Brunswick,Piscataway,08854,United States
2.New Jersey Advanced Manufacturing Institute,Rutgers University-New Brunswick,Piscataway,08854,United States
3.Department of Mechanical & Energy Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Department of Mechanical and Aerospace Engineering,Rutgers University-New Brunswick,Piscataway,08854,United States

Guo，Weihong,Gawade，Vidita,Zhang，Bi,et al. Explainable AI for layer-wise emission prediction in laser fusion[J]. CIRP Annals,2023.

Guo，Weihong,Gawade，Vidita,Zhang，Bi,&Guo，Yuebin.(2023).Explainable AI for layer-wise emission prediction in laser fusion.CIRP Annals.

Guo，Weihong,et al."Explainable AI for layer-wise emission prediction in laser fusion".CIRP Annals (2023).