A statistics-guided approach to dimensional quality characterization of free-form surfaces with an application to 3D printing

Abstract

Free-from surfaces such as three-dimensional (3D) printing products play an important role in customized production of manufacturing. Despite its popularity, 3D printing products often suffer dimensional quality issues due to geometric deformation during the layer-by-layer printing process. Different from traditional production, 3D printing products are often customized and not for mass production, thus it is often difficult to designate tolerance profiles to assess the production quality. To address this challenge, we propose a statistics-guided approach to characterize the dimensional quality for free-form surfaces. The proposed approach can provide local quality measures based on the original customized design and the scanned profile of a printed product. It gives a unified scale of the quality assessment measurements to conveniently compare the quality of products from different customized designs. Case studies and simulation experiments show that, the proposed approach can provide effective quality assessment and characterization for 3D printing.

Keywords:

• Dimensional quality
• Gaussian process interpolation
• multiple testing
• quality assessment
• smoothing

Acknowledgments

The authors thank the editor, and two referees for their helpful comments and suggestions, which have led to improvements in the article.

Additional information

Funding

Miss Hao Wang and Dr. Kaibo Wang’s work are supported by the Key Program of the National Natural Science Foundation of China (Grant No. 71932006 and 71731008).

Notes on contributors

Hao Wang

Hao Wang is currently working toward a PhD degree in the Department of Industrial Engineering, Tsinghua University, Beijing, China. She received the BS degree from Tianjin University, Tianjin, China, in 2010. Her general research interests include statistical modeling, process monitoring in manufacturing processes. Ms. Wang is a member of the Institute for Operations Research and the Management Sciences (INFORMS).

Qiong Zhang

Qiong Zhang is an Assistant Professor in Statistics at Clemson University. She holds PhD degree in Statistics from the University of Wisconsin- Madison. Her research interests include design and analysis for computer experiments, uncertainty quantification, and statistical methods in Engineering. She is a member of ASA and INFORMS.

Kaibo Wang

Kaibo Wang is Professor in the Department of Industrial Engineering, Tsinghua University, Beijing, China. He received his BEng, and MS degrees in Mechatronics and Mechanical Engineering from Xi'an Jiaotong University, Xi'an, China, in 1999 and 2002 respectively, and PhD in Industrial Engineering and Engineering Management from Hong Kong University of Science and Technology, Hong Kong, in 2006. His research focuses on statistical quality control and data-driven system modelling, monitoring, diagnosis and control, with a special emphasis on the integration of engineering knowledge and statistical theories for solving problems from the real industry. He has published more than 40 peer reviewed papers in journals such as Journal of Quality Technology, IISE Transactions, Quality and Reliability Engineering International, IEEE Transactions on Automation Science and Engineering, and others.

Xinwei Deng

Xinwei Deng is an associate professor in the Department of Statistics at Virginia Tech. He received his PhD from Georgia Institute of Technology in 2009. Dr. Deng's research interests include data analytics, machine learning, design of experiments, and interface between experimental design and machine learning. Dr. Deng actively collaborates with researchers in engineering, nanotechnology, bioinformatics, and environmental sciences. He has over 50 peer-reviewed top journal and conference publications.