Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts
Authors:
- Yuhang YangYYYuhang Yang
- Davis J. McGregorDMDavis J. McGregor
- Sameh TawfickSTSameh Tawfick
- William P. KingWKWilliam P. King
- Chenhui ShaoCSChenhui Shao
- 12 January 2022
- journal article
- research article
- Published by Elsevier in Additive Manufacturing
- Vol. 51
Abstract
No abstract availableKeywords
Funding Information
- Zhejiang University
- University of Illinois at Urbana-Champaign
This publication has 38 references indexed in Scilit:
- Elastic and failure response of imperfect three-dimensional metallic lattices: the role of geometric defects induced by Selective Laser MeltingJournal of the Mechanics and Physics of Solids, 2017
- Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A ReviewAdvanced Engineering Materials, 2017
- Dynamic Sampling Design for Characterizing Spatiotemporal Processes in ManufacturingJournal of Manufacturing Science and Engineering, 2017
- Hierarchical Bayesian fatigue data analysisInternational Journal of Fatigue, 2017
- An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emissionMechanical Systems and Signal Processing, 2017
- Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation ModelingJournal of Manufacturing Science and Engineering, 2016
- Review of in-situ process monitoring and in-situ metrology for metal additive manufacturingMaterials & Design, 2016
- Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed ProductsJournal of Manufacturing Science and Engineering, 2014
- Feature selection for manufacturing process monitoring using cross-validationJournal of Manufacturing Systems, 2013
- Multilevel (Hierarchical) Modeling: What It Can and Cannot DoTechnometrics, 2006