A cost-effective and reliable measurement strategy for 3D printed parts by integrating low- and high-resolution measurement systems

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.

KEYWORDS:

• Bayesian generative model
• Laser scanner
• MCMC
• measurement accuracy
• measurement cost

Acknowledgements

The authors greatly acknowledge the efforts of the editor and two referees that have resulted in great improvements of this article.

Additional information

Funding

The research of Wang and Tsung was supported by the Hong Kong RGC General Research Funds 16203917.

Notes on contributors

Kai Wang

Kai Wang is currently a Ph.D. candidate in the Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Hong Kong, China. He received his bachelor’s degree in industrial engineering in 2014 from Xi’an Jiaotong University, Shaanxi, China. His research focuses on statistical modeling and monitoring multimodal data, multi-resolution metrology data fusion and machine learning applications to quality engineering problems.

Fugee Tsung

Fugee Tsung is a professor in the Department of Industrial Engineering and Decision Analytics, Director of the Quality and Data Analytics Lab, at the Hong Kong University of Science and Technology. He is a Fellow of IISE, ASA, ASQ, and an Academician of IAQ. He received both his M.Sc. and Ph.D. from the University of Michigan, Ann Arbor, and his B.Sc. from National Taiwan University. His research interests include quality analytics and industrial big data, statistical process control, monitoring, and diagnosis.