A framework of tolerance specification for freeform point clouds and capability analysis for reverse engineering processes

Abstract

The combination of reverse engineering (RE) with additive manufacturing (AM) is widely used for solid freeform fabrication and overcomes limitations with the current capabilities of designers and CAD packages that are restricted to classical model shapes. The shape flexibility offered by RE typically leads to the generated CAD models being represented in point cloud formats or stereolithography files. One resulting challenge is the difficulty with tolerance specification for the RE-generated models, especially for freeform shapes. Geometric metrology for the AM-produced parts to determine their geometric conformation can also be involved. Furthermore, conventional process analyses cannot be performed directly after RE because of the complex geometric data structure. We propose to address all these issues with a new tolerance specification and process analysis framework based on volumetric data analysis. The tolerance zone constructed under our framework, and the corresponding geometric measurements, are consistent with the profile tolerance in geometric dimensioning and tolerancing standards. Reverse engineering's process capability is assessed under our methodology via a parametric bootstrap procedure in the size-and-shape space. The performance and utility of the proposed framework are validated via a process capability study on RE-generated CAD models of multiple, additively manufactured freeform objects.

Keywords:

• Geometric dimensioning and tolerancing
• parametric bootstrap
• point cloud data
• process capability analysis
• reverse engineering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in the GitHub repo, https://github.com/ZhaohuiGeng/Freeform-Tolerance-Specification-and-PCI-Analysis.

Additional information

Notes on contributors

Zhaohui Geng

Zhaohui Geng is currently an Assistant Professor in the Department of Manufacturing and Industrial Engineering at The University of Texas Rio Grande Valley. His research interests are in the area of statistical shape analysis, statistical machine learning, Bayesian optimisation, and large-scale optimisation, with applications in reverse engineering, additive manufacturing, metrology, manufacturing systems, and product development. He is particularly interested in developing statistical inferential methodologies and statistical/probabilistic machine learning algorithms to solve quality and/or manufacturability-related problems at the intersections of design, metrology, advanced manufacturing, and production systems. Zhaohui is a member of IISE, INFORMS, SME, and ASTM.

Arman Sabbaghi

Arman Sabbaghi is an Associate Professor in the Department of Statistics and Associate Director of the Statistical Consulting Service at Purdue University. He became an Elected Member of the International Statistical Institute in 2020. He received his PhD in Statistics from Harvard University in 2014. Dr. Sabbaghi's research interests include the development of statistical and machine learning algorithms for improved control of complex engineering systems, Bayesian data analysis, experimental design, and causal inference.

Bopaya Bidanda

Bopaya Bidanda is currently the Ernest E. Roth Professor after being the Department Chair for 21 years of the Industrial Engineering Department at the University of Pittsburgh. In addition, he is also the Director of the Manufacturing Assistance Center (MAC) and the Director of the Center for Industry Studies. He is a Fellow of the Institute of Industrial & Systems Engineers. He has published 13 books and well over 100 papers in international journals and conference proceedings. Dr. Bidanda currently serves as the President of the Institute of Industrial & Systems Engineers (IISE). His research focuses on Manufacturing Systems, Reverse Engineering & Rapid Prototyping, Product Development and Project Management.