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December 2018 Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables
Arman Sabbaghi, Qiang Huang
Ann. Appl. Stat. 12(4): 2409-2429 (December 2018). DOI: 10.1214/18-AOAS1158

Abstract

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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Arman Sabbaghi. Qiang Huang. "Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables." Ann. Appl. Stat. 12 (4) 2409 - 2429, December 2018. https://doi.org/10.1214/18-AOAS1158

Information

Received: 1 September 2017; Revised: 1 January 2018; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029460
MathSciNet: MR3875706
Digital Object Identifier: 10.1214/18-AOAS1158

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 4 • December 2018
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