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Intelligent surface roughness measurement using deep learning and computer vision: a promising approach for manufacturing quality control

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Abstract

In modern manufacturing, accurate and efficient assessment of surface finish quality is vital to product integrity within mechanical systems and contribution to its overall performance. For this reason, the present study proposes an intelligent system based on deep learning and computer vision to evaluate the surface roughness of machined steel mechanical parts. In this context, two popular deep learning architectures designed for object detection tasks, namely, Faster R-CNN Inception v2 and Faster R-CNN ResNet50, are used to train and evaluate an automatic detection model at this purpose. The obtained results demonstrate the potential of the proposed hybrid technique and its advantages related to other technique for rapid, accurate, and real-time assessment of surface roughness. Additionally, the model trained using Faster R-CNN Inception v2 emerges as the preferred choice based on the evaluation results in terms of detection accuracy and calculation speed during the training phase. Finally, this article proposes improvements to be made to the model with the aim of its integration into the production process for both offline and online product quality control to ensure intelligent, profitable, and sustainable production.

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Acknowledgements

We would like to acknowledge all responsible of plastic department in LAK school for providing the materials, machine, and instruments used in this paper.

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Authors and Affiliations

  1. Laboratory of Structural Engineering, Processes, Intelligent Systems and Informatique in ENSAM School, Hassan 2 University Casablanca, Casablanca, Morocco

    Mohamed EL Ghadoui & Ahmed Mouchtachi

  2. Laboratory of Complex Cyber Physical Systems in ENSAM School, Hassan 2 University Casablanca, Casablanca, Morocco

    Radouane Majdoul

Authors
  1. Mohamed EL Ghadoui
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  2. Ahmed Mouchtachi
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  3. Radouane Majdoul
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Contributions

This paper is mainly the work of the author Mr. EL G.M. under the supervision of two other people who are the thesis director Mr. M.A. and his co-director Mr. M.R. who participated in the supervision, orientation and discussion of the work.

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Correspondence to Mohamed EL Ghadoui.

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The main data used are presented in article. But, upon reasonable request, the authors are willing to share the all additional data and code request to generate the results in this paper.

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EL Ghadoui, M., Mouchtachi, A. & Majdoul, R. Intelligent surface roughness measurement using deep learning and computer vision: a promising approach for manufacturing quality control. Int J Adv Manuf Technol 129, 3261–3268 (2023). https://doi.org/10.1007/s00170-023-12457-z

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  • DOI: https://doi.org/10.1007/s00170-023-12457-z

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