Comparison and Evaluation of Conventional and Machine Learning-Assisted Reverse Engineering Workflows

Authors

DOI:

https://doi.org/10.20535/2521-1943.2025.9.3(106).333347

Keywords:

reverse engineering, point cloud, CAD reconstruction, machine learning, workflow selection, dimensional accuracy

Abstract

Reverse engineering workflows play a crucial role in converting physical components into digital CAD models, impacting efficiency and accuracy in various industries. Traditional manual approaches, while highly precise, are often slow and resource-intensive, prompting exploration of machine learning (ML) methods promising accelerated results. The aim of this study is to perform a comparative overview and practical evaluation of conventional and ML-assisted reverse engineering workflows, identifying their accuracy, speed, and applicability to support evidence-based recommendations for workflow selection. As part of the study, a steel chuck-jaw was scanned using a high-precision scanner as an example of a part for reverse engineering. Three distinct CAD models were created: the first by manual surfacing in CATIA V5, the second by semi-automatic fitting in Geomagic Design X, and the third using the ML-based Point2CAD pipeline followed by post-processing in Geomagic Design X. The models were then assessed by comparing surface-to-cloud deviations and the total time required for reconstruction. The manual CATIA workflow achieved the best accuracy but demanded significant time and hands-on effort. The semi-automatic Geomagic workflow offered an effective balance between accuracy and efficiency. The Point2CAD approach dramatically reduced reconstruction time but resulted in significant local deviations, even though the overall geometry was acceptably maintained. These results suggest selecting manual workflow for tasks where accuracy is critical, semi-automatic workflow can be recommended for standard precision tasks with balanced effort, and ML-assisted workflow – for rapid prototyping or digital archiving when moderate inaccuracy is permissible and the necessary hardware is available. Additionally, the comparative overview underscores that selecting a suitable reverse engineering workflow depends significantly on project-specific requirements, particularly regarding required accuracy, available hardware, and acceptable processing time.

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Published

2025-09-26

How to Cite

[1]
Y. Lashyna and A. Kunytsia, “Comparison and Evaluation of Conventional and Machine Learning-Assisted Reverse Engineering Workflows”, Mech. Adv. Technol., vol. 9, no. 3(106), pp. 298–308, Sep. 2025.

Issue

Vol. 9 No. 3(106) (2025)

Section

Advanced Mechanical Engineering and Manufacturing Technologies

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