Bayesian Model Building From Small Samples of Disparate Data for Capturing In-Plane Deviation in Additive Manufacturing

Arman Sabbaghi, Qiang Huang, Tirthankar Dasgupta

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Quality control of geometric shape deviation in additive manufacturing relies on statistical deviation models. However, resource constraints limit the manufacture of test shapes, and consequently impede the specification of deviation models for new shape varieties. We present an adaptive Bayesian methodology that effectively combines in-plane deviation data and models for a small sample of previously manufactured, disparate shapes to aid in the model specification of in-plane deviation for a broad class of new shapes. The power and simplicity of this general methodology is demonstrated with illustrative case studies on in-plane deviation modeling for polygons and straight edges in free-form shapes using only data and models for cylinders and a single regular pentagon. Our Bayesian approach facilitates deviation modeling in general, and thereby can help advance additive manufacturing as a high-quality technology. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)532-544
Number of pages13
JournalTechnometrics
Volume60
Issue number4
DOIs
StatePublished - Oct 2 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

Keywords

  • 3D printing
  • Bayesian data analysis
  • Posterior predictive check
  • Statistical shape analysis

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