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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

Author

Listed:
  • Li Wei

    (Guangxi University of Science and Technology
    UCSI University)

  • Mahmud Iwan Solihin

    (UCSI University)

  • Sarah ‘Atifah Saruchi

    (Universiti Malaysia Pahang Al-Sultan Abdullah)

  • Winda Astuti

    (BINUS ASO School of Engineering, Bina Nusantara University)

  • Lim Wei Hong

    (UCSI University)

  • Ang Chun Kit

    (UCSI University)

Abstract

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation.

Suggested Citation

  • Li Wei & Mahmud Iwan Solihin & Sarah ‘Atifah Saruchi & Winda Astuti & Lim Wei Hong & Ang Chun Kit, 2024. "Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review," SN Operations Research Forum, Springer, vol. 5(3), pages 1-71, September.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:3:d:10.1007_s43069-024-00337-5
    DOI: 10.1007/s43069-024-00337-5
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    References listed on IDEAS

    as
    1. Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.
    2. Aqsa Rasheed & Bushra Zafar & Amina Rasheed & Nouman Ali & Muhammad Sajid & Saadat Hanif Dar & Usman Habib & Tehmina Shehryar & Muhammad Tariq Mahmood, 2020. "Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-24, November.
    3. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    4. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
    5. Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
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