Accelerating Additive Design With Probabilistic Machine Learning

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.

Automated summary

This paper introduces a surrogate modeling approach based on experimental data to improve the design efficiency in additive manufacturing (AM) processes. The effectiveness and practicality of the method are demonstrated through its application to multiple benchmark designs of direct energy deposition (DED), including forward prediction, global sensitivity analysis, backward prediction and optimization, and intelligent data addition.