Reconstructing original design: Process planning for reverse engineering

Abstract

Reverse Engineering (RE) has been widely used to extract geometric design information from a physical product for reproduction or redesign purposes. A scan of an object is often implemented to (re-)construct the computer-aided design model. However, this model is most likely an inaccurate representation of the original design, due to the existing uncertainties in each part and the scanning process. This randomness can result in shrinking the original tolerance region or even yielding asymmetric tolerance regions, which can call for unnecessarily high precision reproduction. In this article, we first propose an algorithm to generate the mean configuration based on the data clouds collected from several scans and multiple parts (if applicable). A Bayesian model with prior knowledge of production processes and scanners is specified to model the statistical properties of the mean configuration. Its marginal posterior outperforms single-scan models with lower variances, concentrating around the physical object or initial design. Furthermore, we propose a bi-objective optimization model to address RE process planning questions regarding the required number of scans and parts to achieve target accuracy requirements. Simulations and industrial case studies, including both unique freeform objects and mechanical parts, are conducted to illustrate and evaluate the performances of proposed methods.

Keywords:

• Reverse engineering
• point clouds
• additive manufacturing
• volumetric data analysis
• statistical shape analysis

Additional information

Notes on contributors

Zhaohui Geng

Dr. Zhaohui Geng is currently an Assistant Professor of the Department of Manufacturing and Industrial Engineering at The University of Texas Rio Grande Valley. Zhaohui’s research interests are in the area of statistical shape analysis, statistical machine learning, Bayesian optimization, and large-scale optimization, 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, and ASTM.

Arman Sabbaghi

Dr. 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

Dr. 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.