Reconstructing original design: Process planning for reverse engineering
针对逆向工程中扫描数据随机性导致原始设计失真问题，提出基于贝叶斯模型生成平均构型的方法，并建立双目标优化模型确定所需扫描次数和零件数量，帮助工程师在满足精度要求下高效规划逆向工程工艺。
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.