A cost-effective and reliable measurement strategy for 3D printed parts by integrating low- and high-resolution measurement systems. (3rd October 2018)

• Record Type: Journal Article
• Authors: Wang, Kai; Tsung, Fugee
• Is Part Of: IISE transactions. Volume 50:Number 10(2018)
• Page Start: 900
• Page End: 912
• Publication Date: 2018-10-03
• Subjects: Bayesian generative model -- Laser scanner -- MCMC -- measurement accuracy -- measurement cost; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2018.1455117
• Languages: English
• ISSNs: 2472-5854

Abstract

ABSTRACT: Metrology data are crucial to quality control of three-dimensional (3D) printed parts. Low-cost measurement systems are often unreliable due to their low resolutions, whereas high-resolution measurement systems usually induce high measurement costs. To balance the measurement cost and accuracy, a new cost-effective and reliable measurement strategy is proposed in this article, which jointly uses two-resolution measurement systems. Specifically, only a small sample of base parts are measured by both the low- and high-resolution measurement systems in order to save costs. The measurement accuracy of most parts with only low-resolution metrology data is improved by effectively integrating high-resolution metrology data of the base parts. A Bayesian generative model parameterizes a part-independent bias and variance pattern of the low-resolution metrology data and facilitates a between-part data integration via an efficient Markov chain Monte Carlo sampling algorithm. This multi-part two-resolution metrology data integration highlights the novelty and contribution of this article compared with the existing one-part data integration methods in the literature. Finally, an intensive experimental study involving a laser scanner and a machine visual system has validated the effectiveness of our measurement strategy in acquisition of reliable metrology data of 3D printed parts.