A Hardware–Software Interactive Inspection Framework for Automated Copper Cathode Blank Defect Detection and Robotic Sorting

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan

2 Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.

3 Department of Biomedical Engineering, Meybod University, Meybod, Iran.

4 Senior Expert, Technical & Engineering Unit, R&D Department, Sarcheshmeh Copper Complex, Rafsanjan, Iran.

5 Expert, Refinery and Casting Division, Sarcheshmeh Copper Complex, Rafsanjan, Iran.

6 Technical Safety Supervisor of Mines, Health, Safety and Environment (HSE), Sarcheshmeh Copper Complex, Rafsanjan, Iran.

10.22091/jemsc.2026.15913.1378

Abstract

Automated inspection of copper cathode blanks is important in electrorefining production lines, where surface defects can interfere with stripping operations, damage reusable stainless-steel blanks, and increase downtime. This study proposes a hardware–software interactive framework for automated defect detection and robotic sorting under real industrial conditions at the Sarcheshmeh Copper Complex, Iran. The system integrates an industrial camera, a Time-of-Flight distance sensor for cathode positioning verification, real-time monitoring software, deep learning-based inspection, and PLC-based reject command generation. YOLOv8 segmentation is used to isolate defect-relevant regions, while classification is performed using a lightweight MobileNetV4 network enhanced with Local–Global Attention. To address class imbalance, an α-balanced focal loss was employed. The dataset consisted of 5,489 real production-line images divided into training, validation, and test sets. Experimental results achieved 98.5% accuracy, 96.9% F1-score, and 99.2% AUC, demonstrating the effectiveness of the proposed framework for deployment-oriented automated quality control and robotic sorting.

Keywords

Main Subjects

References

Ahmed, K. R. (2023). DSTEELNet: A real-time parallel dilated CNN with Atrous spatial pyramid pooling for detecting and classifying defects in surface steel strips. Sensors, 23(1), 544. https://doi.org/10.3390/s23010544
Eslami, N., Mohajjel, M., Rasouli Kenari, A., & Abbasi, S. (2025). Energy-Aware multi-exit vision transformer with early exits for efficient inference on energy-harvesting edge Devices. Engineering Management and Soft Computing. https://doi.org/10.22091/jemsc.2025.13413.1289
Ghayoumi Zadeh, H., Fayazi, A., Rezaee, K., Aminaee, A., Halavati, H., Tahernejad, M., Memarzadeh, H., Masoumi, A., & Jafari, M. S. (2026). Automated Surface Defect Detection in Copper Blanks Using YOLOv8 Segmentation and EfficientNetV2-S Classification. Journal of AI and Data Mining, 14(2), 169-181. https://doi.org/10.22044/jadm.2026.16853.2818
Hu, B., & Wang, J. (2020). Detection of PCB surface defects with improved faster-RCNN and feature pyramid network. IEEE Access, 8, 108335–108345. https://doi.org/10.1109/ACCESS.2020.3001349
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017, July). Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2261–2269). Honolulu. https://doi.org/10.1109/CVPR.2017.243
Hütten, N., Alves Gomes, M., Hölken, F., Andricevic, K., Meyes, R., & Meisen, T. (2024). Deep learning for automated visual inspection in manufacturing and maintenance: A survey of open-access papers. Applied System Innovation, 7(1), 11. https://doi.org/10.3390/asi7010011
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
Li, L., Liu, Z., Zhao, H., Xue, L., & Wu, J. (2024). The bearing surface defect detection method combining magnetic particle testing and deep learning. Applied Sciences, 14(5), 1747. https://doi.org/10.3390/app14051747
Lins, R. G., Santos, R. E. D., & Gaspar, R. (2023). Vision-based measurement for quality control inspection in the context of Industry 4.0: A comprehensive review and design challenges. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 45(4). https://doi.org/10.1007/s40430-023-04050-y
Liu, J., Meng, Y., Li, C., & Tie, J. (2024, September). Research on purity control on high-purity copper by electrorefining. In Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024) (Vol. 13261, pp. 627-631). SPIE. https://doi.org/10.1117/12.3046539
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., & Berg, A. C. (2016). SSD: Single shot MultiBox detector. In Lecture Notes in Computer Science (pp. 21–37). https://doi.org/10.1007/978-3-319-46448-0_2
Lu, J., Dou, H., Zhou, Z., Li, H., Wang, Z., Jiang, M., Li, F., Gao, Y., Song, C., Fang, D., He, Y., & Li, Y. (2023). Effect of rapid hollow cathode plasma nitriding treatment on corrosion resistance and friction performance of AISI 304 stainless steel. Materials, 16(24), 7616. https://doi.org/10.3390/ma16247616
Pakzad, A., & Pakzad, A. (2026). Application of deep learning algorithms in clustering production units: Enhancing regulatory processes using soft computing approaches (Case Study: North Khorasan Province). Engineering Management and Soft Computing, 12(1), 82-101. https://doi.org/10.22091/jemsc.2026.13325.1286
Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., Aggarwal, V., Zhu, T., Moro, D., & Howard, A. (2024). MobileNetV4: Universal Models for the Mobile Ecosystem. In Lecture Notes in Computer Science (pp. 78–96). https://doi.org/10.1007/978-3-031-73661-2_5
Sahlman, M., Aromaa, J., & Lundström, M. (2021). Copper cathode contamination by nickel in copper electrorefining. Metals, 11(11), 1758. https://doi.org/10.3390/met11111758
Simonyan, K., & Zisserman, A. (2015, April). Very deep convolutional networks for large-scale image recognition. In 3rd international conference on learning representations (ICLR 2015). Computational and Biological Learning Society. https://doi.org/10.48550/arXiv.1409.1556
Wei, R., & Bi, Y. (2019). Research on recognition technology of aluminum profile surface defects based on deep learning. Materials, 12(10), 1681. https://doi.org/10.3390/ma12101681
Xian, Y., Liu, G., Fan, J., Yu, Y., & Wang, Z. (2021). YOT-Net: YOLOv3 combined triplet loss network for copper elbow surface defect detection. Sensors, 21(21), 7260. https://doi.org/10.3390/s21217260
Xian, Y., Yu, Y., Lian, Y., Fan, J., & Wang, Z. (2023). An EA-based pruning on improved YOLOv3 for rapid copper elbow surface defect detection. Engineering Applications of Artificial Intelligence, 123, 106412. https://doi.org/10.1016/j.engappai.2023.106412
Xie, Y., Hu, W., Xie, S., & He, L. (2023). Surface defect detection algorithm based on feature-enhanced YOLO. Cognitive Computation, 15(2), 565–579. https://doi.org/10.1007/s12559-022-10061-z
Zahra, R., & Rezaeenour, J. (2025). Review community detection algorithms in multilayer networks; Traditional methods and deep learning. Engineering Management and Soft Computing, 10(2), 1-25. https://doi.org/10.22091/jemsc.2025.11154.1196
Zhang, G., Chen, T., & Wang, J. (2025). CSC-YOLO: An image recognition model for surface defect detection of copper strip and plates. Journal of Shanghai Jiaotong University (Science), 30(5), 1037–1049. https://doi.org/10.1007/s12204-024-2723-2
Zhang, Z., Huang, X., Wei, D., Chang, Q., Liu, J., & Jing, Q. (2024). Copper nodule defect detection in industrial processes using deep learning. Information, 15(12), 802. https://doi.org/10.3390/info15120802
Zhao, C., Shu, X., Yan, X., Zuo, X., & Zhu, F. (2023). RDD-YOLO: A modified YOLO for detection of steel surface defects. Measurement, 214, 112776. https://doi.org/10.1016/j.measurement.2023.112776
Zhao, W., Chen, F., Huang, H., Li, D., & Cheng, W. (2021). A new steel defect detection algorithm based on deep learning. Computational Intelligence and Neuroscience, 2021(1). https://doi.org/10.1155/2021/5592878
Marjani, M. R., Jabbari, M., & Aghaee, M. (2026). A Hybrid Approach Based on Data Density Analysis for Group Supplier Selection Problem Considering Multi-Level Risk Assessment: A Case Study. Journal of Data Analytics and Intelligent Decision-making, 2(3), 1-28. doi: 10.22091/jdaid.2026.16147.1066
Bahadori Jahromi, H. (2026). From Analytics to Agility: How Smart Contract Capability and Supply Chain Digitalization Strengthen Supply Chain Performance in Financial Services. Journal of Data Analytics and Intelligent Decision-making, 2(3), 29-54. doi: 10.22091/jdaid.2026.16727.1094
Kiani, M. S. (2026). Sports Law and Coaches' Responsibility: Challenges and New Approaches in Iran. Journal of Data Analytics and Intelligent Decision-making, 2(2), 1-9. doi: 10.22091/jdaid.2026.15415.1035
Molani Aghdam, M., & Ardeshir Larijani, I. (2026). Artificial neural network evaluating nonlinear evolutionary effect of cognitive and risk complexity in Ambidextrous strategy renewal. Journal of Data Analytics and Intelligent Decision-making, 2(2), 10-30. doi: 10.22091/jdaid.2026.15918.1050
Joshan, A. (2026). Analysis of Emerging Approaches in Cybersecurity for Internet of Things (IoT) Networks with an Emphasis on Smart Urban and Domestic Infrastructures. Journal of Data Analytics and Intelligent Decision-making, 2(1), 1-16. doi: 10.22091/jdaid.2026.15424.1036
Jamshidi Gahrouei, A., & Asgharian, R. (2026). AI-Driven Protection Schemes for Modern Power Grids: Technologies, Challenges, and Opportunities. Journal of Data Analytics and Intelligent Decision-making, 2(1), 17-30. doi: 10.22091/jdaid.2026.15133.1032
Shahabi, V., Sarhangi, R., & Mehdiabadi, A. (2025). Explaining IT Governance for Implementing Branchless Banking Through Digital Transformation. Journal of Data Analytics and Intelligent Decision-making, 1(4), 1-31. doi: 10.22091/jdaid.2026.14530.1018
Joshan, A. (2025). Towards a Human-Centric Industry 5.0 Enabled by the Convergence of Artificial Intelligence, Internet of Things, and Blockchain. Journal of Data Analytics and Intelligent Decision-making, 1(4), 32-49. doi: 10.22091/jdaid.2026.14749.1025
Riki, M., Mohammadi, F., & Eslami, E. (2025). Video Coding Machine Architecture for Smart Urban Traffic Optimization with Deep Learning. Journal of Data Analytics and Intelligent Decision-making, 1(3), 1-12. doi: 10.22091/jdaid.2025.14036.1003
Mansoori, A. (2025). The Application of Artificial Intelligence in Human Resource Performance Appraisal: A Conceptual Framework for Responsible Implementation. Journal of Data Analytics and Intelligent Decision-making, 1(3), 13-30. doi: 10.22091/jdaid.2025.14114.1009
Jafari, H., Etebari, A., & Jafari, M. (2025). Proposing a Developed Gram–Schmidt Algorithm to Construct Orthogonal Unit Vectors (A Mathematical and Practical Approach). Journal of Data Analytics and Intelligent Decision-making, 1(2), 61-78. doi: 10.22091/jdaid.2025.14004.1001
Hasani, M., Deiranlou, M., & Karimi, H. (2025). Designing an Efficient Bike-Sharing System for the University of Bojnord. Journal of Data Analytics and Intelligent Decision-making, 1(2), 79-89. doi: 10.22091/jdaid.2025.14320.1014
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