Abstract Additive manufacturing (AM) has been growing rapidly to transform industrial applications. However, the fundamental mechanism of AM has not been fully understood which resulted in low success rate of building. A remedy is to introduce surrogate modeling based on experimental dataset to assist additive design and increase design efficiency. As one of the first papers for predictive modeling of AM especially direct energy deposition (DED), this paper discusses a bidirectional modeling framework and its application to multiple DED benchmark designs including: (1) forward prediction with cross-validation, (2) global sensitivity analyses, (3) backward prediction and optimization, and (4) intelligent data addition. Approximately 1150 mechanical tensile test samples were extracted and tested with input variables from machine parameters, postprocess, and output variables from mechanical, microstructure, and physical properties.

Yiming Zhang

Sreekar Karnati

Soumya Nag

Neil Johnson

Genghis Khan

Brandon Ribic

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Accelerating Additive Design With Probabilistic Machine Learning

A bidirectional modeling framework for predictive modeling of AM especially Direct Energy Deposition and its application to multiple DED benchmark designs including (1) Forward Prediction with Cross Validation, (2) Global Sensitivity Analyses, (3) Backward Prediction and Optimization, (4) Intelligent Data Addition.

加速增材设计与概率机器学习

Accelerating Additive Design With Probabilistic Machine Learning

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Journal Info

Category	Quartile
ENGINEERING, MULTIDISCIPLINARY	2