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Proceeding Paper

Study on 3D Computer-Aided Feature Inspection for Product Design and Analysis †

by
Chengsi Lin
*,
Muhamad Arfauz A. Rahman
and
Paul G. Maropoulos
School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast BT7 1NN, UK
*
Author to whom correspondence should be addressed.
Presented at the 39th International Manufacturing Conference, Derry/Londonderry, UK, 24–25 August 2023.
Eng. Proc. 2024, 65(1), 13; https://doi.org/10.3390/engproc2024065013
Published: 11 March 2024
(This article belongs to the Proceedings of The 39th International Manufacturing Conference)

Abstract

:
The aim of this project is to verify the suitability of three-dimensional (3D) scanners with computer-aided systems as a new method of geometric feature inspection. Three-dimensional scanners are widely used for field recording and model reconstruction. However, at present, 3D scanning is rarely used in the field of inspection in the production process. This paper uses the Artec Leo 3D scanner as the basic equipment, randomly selects three different shapes of products for scanning, and then uses computer-aided software to optimize and feature the model. The analysis of the characteristics of the measurement data shows that the scanner has the potential to be used as an inspection tool in industrial production processes.

1. Introduction

Despite the rapid development of non-contact 3D scanners, their application in the industrial field is still relatively rare, especially in the inspection process of industrial production lines [1]. In this context, manual inspection and semi-automatic inspection still account for most of the inspection process. How to improve production efficiency, save costs, and improve product quality is the main problem that enterprises face in the process of product development [2,3]. As an accurate and comprehensive aid, 3D scanning has the potential to replace the human eye and other inspection tools to measure the quality and characteristics of objects. In previous work, CCD and image analysis software were used to build automated inspection systems for inspecting the quality of sandwiches produced in automation [4,5]. Semiconductor devices are also used for individual and batch process inspection, and a potential automation scheme is proposed [6].
This project uses Artec Leo 3D Scanner as the base equipment. Three products with different shapes are randomly selected first for scanning, and then the scanned model is optimized and inspects the model features by using computer-aided software.

2. Methodology

2.1. Scaning of the Products

As shown in Figure 1, three products with different features and inspection difficulties are selected, then scanned in order from simple to difficult. The product is placed on a background that is different from its own colour, and the handheld scanner scans the product at about 1.5 m from the product. After the first scan, the product is flipped so that the side in contact with the background is exposed, and then a second scan is performed. Then, it is imported to the Artec Studio. Figure 2 shows the results of scanning the front and back sides of the same product. They are shown in different colors; Artec Studio is used to erase the excess lines and then align them together to repair the product.

2.2. Measurement of Features

The processed scan products are exported to Geomagic Design X for data analysis. First, the model’s three-dimensional coordinate system is built based on the product’s centre point (as shown in Figure 3). Then, the three-dimensional datum of the model is selected as the base plane to enter sketch mode (as shown in Figure 4). Finally, the outlines of the products are drawn using sketches, and the main characteristic data of the items are measured according to the contour information.

3. Results and Discussion

3.1. Model Feature

Regarding the feature measurements of the model, the characteristic data and projection of the model are based on the three-dimensional coordinate plane and the desired measured features as shown in Figure 5, Figure 6 and Figure 7, respectively.

3.2. Compare with a Manual Inspection Method

As shown in Figure 8 and Figure 9, by comparing the calculation results of computer-aided software with the manual inspection results, it can be seen that the error between them is roughly 1 mm, and the results obtained by 3D scanning can achieve an accuracy of 0.0001 mm, which is more precise.

4. Conclusions

Artec Leo 3D scanners have a fast scanning speed, allowing them to quickly scan objects and generate corresponding digital 3D models. However, in the scanning process, it is preferable to use a background that is different from the color of the object, making it convenient for the scanner to distinguish the product from the background and reduce the difficulty of post-processing. Moreover, if the material of the scanned product is transparent and/or highly reflective, a reflective coating that is easy to clean needs to be applied to the outside of the product.
As can be seen from the results, the Artec Leo 3D scanner has a high level of accuracy. Under the calculation of computer-aided software, it can reach 0.0001 mm. Moreover, the scanner performs extremely well in measuring different shapes and features. Through the measurement comparison of the two qualified products, it can also be verified that the scanning accuracy is high, and the error range is controlled within 1 mm. This provides an important guarantee for the scanner as an inspection tool.

Author Contributions

Conceptualization, C.L. and M.A.A.R.; methodology, C.L. and M.A.A.R.; software, C.L.; validation, C.L.; formal analysis, C.L.; investigation, C.L.; resources, C.L.; data curation, C.L.; writing—original draft preparation, C.L.; writing—review and editing, C.L. and M.A.A.R.; visualization, C.L.; supervision, M.A.A.R. and P.G.M.; project administration, C.L.; funding acquisition, M.A.A.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created.

Acknowledgments

The authors are grateful to the academic and technical staff in the School of Mechanical and Aerospace Engineering, Queen’s University Belfast, for providing material support and other helpful information.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Helle, R.H.; Lemu, H.G. A case study on use of 3D scanning for reverse engineering and quality control. Mater. Today Proc. 2021, 45, 5255–5262. [Google Scholar] [CrossRef]
  2. Ghobakhloo, M. Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 2020, 252, 119869. [Google Scholar] [CrossRef]
  3. Sony, M.; Naik, S. Key ingredients for evaluating Industry 4.0 readiness for organizations: A literature review. Benchmarking Int. J. 2020, 27, 2213–2232. [Google Scholar] [CrossRef]
  4. Lin, C.; Rahman, M.A.A.; Maropoulos, P.G. Enhancing the quality inspection process in the food manufacturing industry through automation. In Proceedings of the 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), Istanbul, Turkey, 17–20 May 2022; Volume 1, pp. 379–384. [Google Scholar]
  5. Lin, C.; Rahman, A.A.; Maropoulos, P. Reconfiguration of quality inspection approach to the food processing industry. In Proceedings of the 38th International Manufacturing Conference (IMC38), Dublin, Ireland, 30 August 2022; pp. 207–214. [Google Scholar]
  6. Rahman, M.A.A.; Lin, C.; Maropoulus, P.G.; Teoh, W.S.; Rahman, A.A.A.; Mohamad, E. Design and Validation of an Automation Strategy for the Strip Test Process in the Semiconductor Industry. In Proceedings of the 2023 International Conference on Robotics and Automation in Industry (ICRAI), Peshawar, Pakistan, 3–5 March 2023; pp. 1–6. [Google Scholar]
Figure 1. Products for scanning inspection.
Figure 1. Products for scanning inspection.
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Figure 2. Import the project.
Figure 2. Import the project.
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Figure 3. The 3D coordinate system of the model.
Figure 3. The 3D coordinate system of the model.
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Figure 4. Enter sketch mode.
Figure 4. Enter sketch mode.
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Figure 5. Simple model features.
Figure 5. Simple model features.
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Figure 6. Simple model features.
Figure 6. Simple model features.
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Figure 7. Simple model features.
Figure 7. Simple model features.
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Figure 8. Computer-aided software inspects the length of the simple model.
Figure 8. Computer-aided software inspects the length of the simple model.
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Figure 9. Manual inspection of the length of the simple model.
Figure 9. Manual inspection of the length of the simple model.
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MDPI and ACS Style

Lin, C.; Rahman, M.A.A.; Maropoulos, P.G. Study on 3D Computer-Aided Feature Inspection for Product Design and Analysis. Eng. Proc. 2024, 65, 13. https://doi.org/10.3390/engproc2024065013

AMA Style

Lin C, Rahman MAA, Maropoulos PG. Study on 3D Computer-Aided Feature Inspection for Product Design and Analysis. Engineering Proceedings. 2024; 65(1):13. https://doi.org/10.3390/engproc2024065013

Chicago/Turabian Style

Lin, Chengsi, Muhamad Arfauz A. Rahman, and Paul G. Maropoulos. 2024. "Study on 3D Computer-Aided Feature Inspection for Product Design and Analysis" Engineering Proceedings 65, no. 1: 13. https://doi.org/10.3390/engproc2024065013

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