期刊
ANNALS OF APPLIED STATISTICS
卷 12, 期 4, 页码 2409-2429出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/18-AOAS1158
关键词
3D printing; Bayesian learning; transfer learning
资金
- NSF [CMMI-1744121, CMMI-1744123]
Shape deviation models constitute an important component in quality control for additive manufacturing (AM) systems. However, specified models have a limited scope of application across the vast spectrum of processes in a system that are characterized by different settings of process variables, including lurking variables. We develop a new effect equivalence framework and Bayesian method that enables deviation model transfer across processes in an AM system with limited experimental runs. Model transfer is performed via inference on the equivalent effects of lurking variables in terms of an observed factor whose effect has been modeled under a previously learned process. Studies on stereolithography illustrate the ability of our framework to broaden both the scope of deviation models and the comprehensive understanding of AM systems.
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