INTEGRATING COMPUTER VISION AND MECHATRONICS FOR AUTOMATED QUALITY CONTROL IN SMART PRODUCT MANUFACTURING
Abstract
Smart manufacturing’s (Industry 4.0) complexity demands automated quality control (AQC), as manual inspection is a major bottleneck. A critical gap exists in integrating “passive” Computer Vision (CV) detection with “active” mechatronic intervention, creating a “siloed” research problem. This research aims to design, develop, and validate a closed-loop AQC framework, integrating deep learning CV and mechatronics to autonomously perform the full QC cycle from detection to real-time physical intervention. An experimental systems integration design was employed. A Convolutional Neural Network (CNN) was trained on a 17,000-image dataset. A Robotic Operating System (ROS) framework was utilized as the integration layer for “hand-eye” calibration, synchronizing the CV node with a 6-axis robotic arm on a test rig. The CV model achieved 99.7% mAP (42ms latency) and calibration yielded ±0.35mm precision. The fully integrated system validation achieved a 99.15% Defect Detection Rate (DDR), a 0.11% False Positive Rate (FPR), and a 97.4% Successful Rejection Rate (SRR). The research empirically validates a holistic, closed-loop AQC framework, successfully solving the “siloed” gap. The system provides a proven, scalable blueprint for moving beyond passive detection to fully autonomous quality control in smart manufacturing.
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References
Adhicandra, I., Kaaffah, F. M., Maharaja, C. H., & Sabri, S. (2024). The Impact of Implementing Blockchain Technology in Learning on Data Security and Integrity. Journal of Computer Science Advancements, 2(1), 1–18. https://doi.org/10.70177/jsca.v2i1.927
Ahn, J. C., & Shah, V. H. (2024). Chapter 49—Artificial intelligence in gastroenterology and hepatology. Dalam C. Krittanawong (Ed.), Artificial Intelligence in Clinical Practice (hlm. 443–464). Academic Press. https://doi.org/10.1016/B978-0-443-15688-5.00016-4
Bernovschi, D., Giacomini, A., Rosati, R., & Romeo, L. (2024). Mitigating Bias in Aesthetic Quality Control Tasks: An Adversarial Learning Approach. 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), 232, 719–725. https://doi.org/10.1016/j.procs.2024.01.071
Blay, K. B., Darko, A., Hwang, S., Brilakis, I., Foster, F., & Wei, R. (2025). Ensuring information security resilience in Digital-enabled Construction Projects (DCP) through quantum security technologies. Automation in Construction, 179, 106480. https://doi.org/10.1016/j.autcon.2025.106480
Bo, J., Liu, Z., wang, Y., & Luo, Q. (2025). Research progress on the application of artificial intelligence in colonoscopy. Gastroenterology & Endoscopy. https://doi.org/10.1016/j.gande.2025.10.003
Bo?ejko, W., Trotskyi, S., Uchro?ski, M., & Wodecki, M. (2025). Optimizing two-machine scheduling in flexible manufacturing systems using autonomous AI and quantum computing. Neurocomputing, 132067. https://doi.org/10.1016/j.neucom.2025.132067
Butera, C., Kaplan, J., Kilroy, E., Harrison, L., Jayashankar, A., Loureiro, F., & Aziz-Zadeh, L. (2023). The relationship between alexithymia, interoception, and neural functional connectivity during facial expression processing in autism spectrum disorder. Neuropsychologia, 180, 108469. https://doi.org/10.1016/j.neuropsychologia.2023.108469
Calderón, C., & Lämmerhofer, M. (2023). Chapter 3—Basic principles for the selection of liquid chromatographic modes for specific applications. Dalam S. Fanali, B. Chankvetadze, P. R. Haddad, C. F. Poole, & M.-L. Riekkola (Ed.), Liquid Chromatography (Third Edition) (Vol. 2, hlm. 81–157). Elsevier. https://doi.org/10.1016/B978-0-323-99969-4.00101-7
Chawla, D., & Mehra, P. S. (2023). A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions. Internet of Things, 24, 100950. https://doi.org/10.1016/j.iot.2023.100950
Chien, C.-F., Hong Van Nguyen, T., Li, Y.-C., & Chen, Y.-J. (2023). Bayesian decision analysis for optimizing in-line metrology and defect inspection strategy for sustainable semiconductor manufacturing and an empirical study. Computers & Industrial Engineering, 182, 109421. https://doi.org/10.1016/j.cie.2023.109421
Chinnasamy, R., Subramanian, M., Easwaramoorthy, S. V., & Cho, J. (2025). Deep learning-driven methods for network-based intrusion detection systems: A systematic review. ICT Express, 11(1), 181–215. https://doi.org/10.1016/j.icte.2025.01.005
Dhaliwal, J., & Walsh, C. M. (2023). Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications. Pediatric Endoscopy, 33(2), 291–308. https://doi.org/10.1016/j.giec.2022.12.001
Dilmaghani, S., & Coelho-Prabhu, N. (2023). Role of Artificial Intelligence in Colonoscopy: A Literature Review of the Past, Present, and Future Directions. Colorectal Cancer Screening Part II, 25(4), 399–412. https://doi.org/10.1016/j.tige.2023.03.002
Ding, Y., Bi, Q., Huang, D., Liao, J., Yang, L., Luo, X., Yang, P., Li, Y., Yao, C., Wei, W., Zhang, J., Li, J., Huang, Y., & Guo, D. (2023). A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species. Food Chemistry, 426, 136670. https://doi.org/10.1016/j.foodchem.2023.136670
Elmousalami, H., Maxy, M., Hui, F. K. P., & Aye, L. (2025). AI in automated sustainable construction engineering management. Automation in Construction, 175, 106202. https://doi.org/10.1016/j.autcon.2025.106202
Guo, K., Li, H., Li, B., & Liang, N. (2024). ResNet-101 based anomaly detection in additive manufacturing: Thermal modeling for quality control in heat exchanger production. Thermal Science and Engineering Progress, 55, 102923. https://doi.org/10.1016/j.tsep.2024.102923
Ilyas, Z., Nandasiri, R., Ali Redha, A., & Aluko, R. E. (2024). Chapter ten—High-performance liquid chromatography coupled with associated column and mass spectroscopic methods for honey analysis. Dalam G. A. Nayik, J. Uddin, & V. Nanda (Ed.), Advanced Techniques of Honey Analysis (hlm. 259–285). Academic Press. https://doi.org/10.1016/B978-0-443-13175-2.00006-4
Imam, M., Adam, S., Dev, S., & Nesa, N. (2024). Air quality monitoring using statistical learning models for sustainable environment. Intelligent Systems with Applications, 22, 200333. https://doi.org/10.1016/j.iswa.2024.200333
Jones, A., Acquaviva, A., Resch, J., & Soliven, A. (2025). 2.28—Analytical derivatization techniques. Dalam M. Soylak (Ed.), Comprehensive Sampling and Sample Preparation (Second Edition) (hlm. 649–690). Academic Press. https://doi.org/10.1016/B978-0-443-15978-7.00105-3
Juricic, S., Rabouille, M., Challansonnex, A., Jay, A., Thébault, S., Rouchier, S., & Bouchié, R. (2023). The Sereine test: Advances towards short and reproducible measurements of a whole building heat transfer coefficient. Energy and Buildings, 299, 113585. https://doi.org/10.1016/j.enbuild.2023.113585
Kim, H. J., Parsa, N., & Byrne, M. F. (2024). The role of artificial intelligence in colonoscopy. Technologic Advances in Colon and Rectal Surgery, 35(1), 101007. https://doi.org/10.1016/j.scrs.2024.101007
Lature, Y., Waruwu, L., Waruwu, L. M., & Zalukhu, C. A. N. (2024). Implementation of Competency-Based Curriculum in Improving the Quality of Education in Schools. Journal of Computer Science Advancements, 2(1), 19–26. https://doi.org/10.70177/jsca.v2i1.1084
Leontaris, L., Mitsiaki, A., Charalampous, P., Dimitriou, N., Leivaditou, E., Karamanidis, A., Margetis, G., Apostolakis, K. C., Pantoja, S., Stephanidis, C., Tzovaras, D., & Papageorgiou, E. (2023). A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency. Computers in Industry, 149, 103919. https://doi.org/10.1016/j.compind.2023.103919
Li, X., Peng, Y., Tian, Q., Feng, T., Wang, W., Cao, Z., & Song, X. (2023). A decomposition-based optimization method for integrated vehicle charging and operation scheduling in automated container terminals under fast charging technology. Transportation Research Part E: Logistics and Transportation Review, 180, 103338. https://doi.org/10.1016/j.tre.2023.103338
Lin, Y.-H., Mao, W.-L., & Fathurrahman, H. I. K. (2024). Development of intelligent Municipal Solid waste Sorter for recyclables. Waste Management, 174, 597–604. https://doi.org/10.1016/j.wasman.2023.12.040
M, A. H., Simamora, R., & Ulwi, K. (2024). Implementation of Agent Systems in Big Data Management: Integrating Artificial Intelligence for Data Mining Optimization. Journal of Computer Science Advancements, 2(1), 33–47. https://doi.org/10.70177/jsca.v2i1.1210
Martini, M., Rosati, R., Romeo, L., & Mancini, A. (2024). Data augmentation strategy for generating realistic samples on defect segmentation task. 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), 232, 1597–1606. https://doi.org/10.1016/j.procs.2024.01.157
Muyammina, I., Safira, A., & Hozairi, H. (2024). Implementation of the Shortest Path Method with Excel Solver to Optimize Goods Delivery Routes. Journal of Computer Science Advancements, 2(1), 27–32. https://doi.org/10.70177/jsca.v2i1.1137
Nikoli?, D., Kosti?, J., ?or?evi? Aleksi?, J., Sunjog, K., Raškovi?, B., Poleksi?, V., Pavlovi?, S., Borkovi?-Miti?, S., Dimitrijevi?, M., Stankovi?, M., & Radoti?, K. (2024). Effects of mining activities and municipal wastewaters on element accumulation and integrated biomarker responses of the European chub (Squalius cephalus). Chemosphere, 365, 143385. https://doi.org/10.1016/j.chemosphere.2024.143385
Quezada, V., Guzmán-Satoque, P., Rincón-Garcia, M. C., Reyes, L. H., & Cruz, J. C. (2025). Chapter 12—Physicochemical and biochemical characterization of antimicrobial peptides. Dalam L. H. Reyes, J. C. Cruz, & G. R. Wiedman (Ed.), Antimicrobial Peptides (hlm. 259–299). Elsevier. https://doi.org/10.1016/B978-0-443-15393-8.00012-9
Raeesi, R., Sahebjamnia, N., & Mansouri, S. A. (2023). The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions. European Journal of Operational Research, 310(3), 943–973. https://doi.org/10.1016/j.ejor.2022.11.054
Rathore, A. S., Guttman, A., Shrivastava, A., & Joshi, S. (2023). Recent progress in high-throughput and automated characterization of N-glycans in monoclonal antibodies. TrAC Trends in Analytical Chemistry, 169, 117397. https://doi.org/10.1016/j.trac.2023.117397
Sasseville, M., Supper, W., Gartner, J.-B., Layani, G., Amil, S., Sheffield, P., Gagnon, M.-P., Hudon, C., Lambert, S., Attisso, E., Ouellet, S., Breton, M., Poitras, M.-E., Roux-Lévy, P.-H., Plaisimond, J., Bergeron, F., Ashcroft, R., Wong, S. T., Groulx, A., … LeBlanc, A. (2025). Electronic Implementation of Patient-Reported Outcome Measures in Primary Health Care: Mixed Methods Systematic Review. Journal of Medical Internet Research, 27. https://doi.org/10.2196/63639
Shi, R., Luo, J., Zhou, N., Liu, Y., Hong, C., Zhang, X.-P., & Chen, X. (2025). Phy-APMR: A physics-informed air pollution map reconstruction approach with mobile crowd-sensing for fine-grained measurement. Building and Environment, 272, 112634. https://doi.org/10.1016/j.buildenv.2025.112634
Wang, H., Zhang, X., Xia, Y., & Wu, X. (2023). An intelligent blockchain-based access control framework with federated learning for genome-wide association studies. Computer Standards & Interfaces, 84, 103694. https://doi.org/10.1016/j.csi.2022.103694
Wang, Z., Liu, Y., & Niu, X. (2023). Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Seminars in Cancer Biology, 93, 83–96. https://doi.org/10.1016/j.semcancer.2023.04.009
Yu, M., Liu, X., Xu, Z., He, L., Li, W., & Zhou, Y. (2023). Automated rail-water intermodal transport container terminal handling equipment cooperative scheduling based on bidirectional hybrid flow-shop scheduling problem. Computers & Industrial Engineering, 186, 109696. https://doi.org/10.1016/j.cie.2023.109696
Yusuf, D., Guilin, X., & Jiao, D. (2023). Application of K-Means Clustering Algorithm to Obtain Recommendations for Strategies to Increase the Number of Students in the Information Systems Study Program at ITB Ahmad Dahlan Jakarta. Journal of Computer Science Advancements, 1(4), 204–214. https://doi.org/10.70177/jsca.v1i4.581
Zhang, C., Liu, S., Hu, H., Xue, J., & Gou, Y. (2024). A hybrid SgDT framework for risk analysis of container-handling operations at automated container terminals. Ocean & Coastal Management, 257, 107321. https://doi.org/10.1016/j.ocecoaman.2024.107321
Zhou, R., Seong, Y., & Liu, J. (2024). Review of the development of hydrological data quality control in Typhoon Committee Members. Tropical Cyclone Research and Review, 13(2), 113–124. https://doi.org/10.1016/j.tcrr.2024.06.003
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