Accelerating Additive Design With Probabilistic Machine Learning
Open Access
- 1 March 2022
- journal article
- research article
- Published by ASME International in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B. Mechanical Engineering
- Vol. 8 (1)
- https://doi.org/10.1115/1.4051699
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.Keywords
Funding Information
- Air Force Research Laboratory (FA8650-16-2-5700)
This publication has 12 references indexed in Scilit:
- Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured AlloysComputer Modeling in Engineering & Sciences, 2018
- Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steelThe International Journal of Advanced Manufacturing Technology, 2017
- An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and controlAdditive Manufacturing, 2015
- Faster Exact Algorithms for Computing Expected Hypervolume ImprovementPublished by Springer Nature ,2015
- Metal Additive Manufacturing: A ReviewJournal of Materials Engineering and Performance, 2014
- Special Section on Multidisciplinary Design Optimization: Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come?AIAA Journal, 2014
- Fabrication of Metal and Alloy Components by Additive Manufacturing: Examples of 3D Materials ScienceJournal of Materials Research and Technology, 2012
- General formulation of HDMR component functions with independent and correlated variablesJournal of Mathematical Chemistry, 2011
- Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functionsStructural and Multidisciplinary Optimization, 2009
- Surrogate-based analysis and optimizationProgress in Aerospace Sciences, 2005