Bayesian additive modeling for quality control of 3D printed products

Arman Sabbaghi, Qiang Huang, Tirthankar Dasgupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Three-dimensional (3D) printing is a disruptive technology with the potential to revolutionize manufacturing. However, control of product boundary deformation is a major issue that can limit its impact in practice. The fundamental requirement for quality control is a generic methodology that can predict deformations for a wide range of designs based on the available data of a few previously manufactured products, potentially of different designs. We develop a Bayesian methodology to effectively update prior conceptions of deformation for a new design based on printed products of different shapes. Our approach is applied to infer deformation models for regular polygons based on deformation models and data for circles. Ultimately, our methodology fills a gap in comprehensive quality control for 3D printing, and can advance it as a high-impact manufacturing technology.

Original languageEnglish (US)
Title of host publication2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publicationAutomation for a Sustainable Future, CASE 2015
PublisherIEEE Computer Society
Pages906-911
Number of pages6
ISBN (Electronic)9781467381833
DOIs
StatePublished - Oct 7 2015
Externally publishedYes
Event11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden
Duration: Aug 24 2015Aug 28 2015

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2015-October
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other11th IEEE International Conference on Automation Science and Engineering, CASE 2015
CountrySweden
CityGothenburg
Period8/24/158/28/15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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