The Annals of Applied Statistics

Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables

Arman Sabbaghi and Qiang Huang

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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.

Article information

Ann. Appl. Stat., Volume 12, Number 4 (2018), 2409-2429.

Received: September 2017
Revised: January 2018
First available in Project Euclid: 13 November 2018

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3D printing Bayesian learning transfer learning


Sabbaghi, Arman; Huang, Qiang. Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables. Ann. Appl. Stat. 12 (2018), no. 4, 2409--2429. doi:10.1214/18-AOAS1158.

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