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Sustainable manufacturing using Zero Defect Manufacturing

A technology maturity assessment framework for Industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing

, , , &
Received 18 May 2023, Accepted 09 Oct 2023, Published online: 17 Oct 2023

References

  • Abagiu, M. M., D. Cojocaru, F. L. Manta, and A. Mariniuc. 2021. “Detection of a Surface Defect on an Engine Block Using Computer Vision.” In 2021 22nd International Carpathian Control Conference (ICCC), 1–5. IEEE. https://ieeexplore.ieee.org/document/9454615.
  • Akbar, M., and T. Irohara. 2018. “Scheduling for Sustainable Manufacturing: A Review.” Journal of Cleaner Production 205:866–883. https://doi.org/10.1016/j.jclepro.2018.09.100.
  • Akundi, A., M. Reyna, S. Luna, and E. Chumacero. 2022. “Automated Quality Control System for Product Dimensional and Surface Analysis – An Industry Case Study.” In 2022 17th Annual System of Systems Engineering Conference (SOSE), 60–65. IEEE.
  • Alonso, V., A. Dacal-Nieto, L. Barreto, A. Amaral, and E. Rivero. 2019. “Industry 4.0 Implications in Machine Vision Metrology: An Overview.” Procedia Manufacturing 41:359–366. https://doi.org/10.1016/j.promfg.2019.09.020.
  • Amrina, E., and S. M. Yusof. 2011. “Key Performance Indicators for Sustainable Manufacturing Evaluation in Automotive Companies.” In 2011 IEEE International Conference on Industrial Engineering and Engineering Management, 1093–1097. IEEE.
  • An, S., X. Zhang, D. Wei, H. Zhu, J. Yang, and K. A. Tsintotas. 2022. “Fasthand: Fast Monocular Hand Pose Estimation on Embedded Systems.” Journal of Systems Architecture 122:102361. https://doi.org/10.1016/j.sysarc.2021.102361.
  • An, S., F. Zhou, M. Yang, H. Zhu, C. Fu, and K. A. Tsintotas. 2021. “Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems.” In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 55–62. IEEE.
  • An, S., H. Zhu, D. Wei, K. A. Tsintotas, and A. Gasteratos. 2022. “Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs.” Journal of Field Robotics 39 (4): 473–493. https://doi.org/10.1002/rob.v39.4.
  • Aslam, M., T. M. Khan, S. S. Naqvi, G. Holmes, and R. Naffa. 2019. “On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey.” IEEE Access 7:176065–176086. https://doi.org/10.1109/Access.6287639.
  • Ayyad, A., M. Halwani, D. Swart, R. Muthusamy, F. Almaskari, and Y. Zweiri. 2023. “Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems.” Robotics and Computer-Integrated Manufacturing 79:102419. https://doi.org/10.1016/j.rcim.2022.102419.
  • Azamfirei, V., Y. Lagrosen, and A. Granlund. 2021. “Harmonising Design and Manufacturing: A Quality Inspection Perspective.” In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1–7. IEEE.
  • Azamfirei, V., F. Psarommatis, and Y. Lagrosen. 2023. “Application of Automation for in-line Quality Inspection, a Zero-defect Manufacturing Approach.” Journal of Manufacturing Systems 67:1–22. https://doi.org/10.1016/j.jmsy.2022.12.010.
  • Bagga, P., K. Bajaj, M. Makhesana, and K. Patel. 2022. “An Online Tool Life Prediction System for CNC Turning Using Computer Vision Techniques.” Materials Today: Proceedings 62:2689–2693.
  • Bakon, K., T. Holczinger, Z. Süle, S. Jaskó, and J. Abonyi. 2022. “Scheduling Under Uncertainty for Industry 4.0 and 5.0.” IEEE Access 10:74977–75017. https://doi.org/10.1109/ACCESS.2022.3191426.
  • Balakera, N., F. K. Konstantinidis, G. Tsimiklis, E. Latsa, and A. Amditis. 2023. “IIoT Network System from Data Collection to Cyber-Physical System Transmission Under the Industry 5.0 Era.” In International Congress on Information and Communication Technology, edited by X. S. Yang, R. S. Sherratt, N. Dey, and A. Joshi, 929–941. Singapore: Springer.
  • Balaska, V., D. Folinas, F. K. Konstantinidis, and A. Gasteratos. 2022. “Smart Counting of Unboxed Stocks in the Warehouse 4.0 Ecosystem.” In 2022 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Bauer, P., A. M. Flores, and G. Reinhart. 2019. “Free-form Surface Analysis and Linking Strategies for High Registration Accuracy in Quality Assurance Applications.” Procedia CIRP 81:968–973. https://doi.org/10.1016/j.procir.2019.03.236.
  • Bhatia, M. S., and S. Kumar. 2020. “Critical Success Factors of Industry 4.0 in Automotive Manufacturing Industry.” IEEE Transactions on Engineering Management 69:2439–2453. https://doi.org/10.1109/TEM.2020.3017004.
  • Birdal, T., E. Bala, T. Eren, and S. Ilic. 2016. “Online Inspection of 3D Parts via a Locally Overlapping Camera Network.” In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 1–10. IEEE.
  • Block, S. B., R. D. da Silva, L. B. Dorini, and R. Minetto. 2020. “Inspection of Imprint Defects in Stamped Metal Surfaces Using Deep Learning and Tracking.” IEEE Transactions on Industrial Electronics 68 (5): 4498–4507. https://doi.org/10.1109/TIE.41.
  • Booth, A.. 2016. “Searching for Qualitative Research for Inclusion in Systematic Reviews: A Structured Methodological Review.” Systematic Reviews 5 (1): 1–23.
  • Burgos Simon, M. A., E. Garro Crevillen, M. Llacer Sanfernando, F. Blanquer, T. Leino, and F. Konstantinidis. 2023. “A Vision-Based Application for Container Detection in Ports 4.0.” In Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, 557–561.
  • Canetta, L., A. Barni, and E. Montini. 2018. “Development of a Digitalization Maturity Model for the Manufacturing Sector.” In 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 1–7. IEEE.
  • Cannizzaro, D., A. G. Varrella, S. Paradiso, R. Sampieri, Y. Chen, A. Macii, E. Patti, and S. Di Cataldo. 2021. “In-situ Defect Detection of Metal Additive Manufacturing: An Integrated Framework.” IEEE Transactions on Emerging Topics in Computing 10 (1): 74–86. https://doi.org/10.1109/TETC.2021.3108844.
  • Cannizzaro, D., A. G. Varrella, S. Paradiso, R. Sampieri, E. Macii, E. Patti, and S. Di Cataldo. 2021. “Image Analytics and Machine Learning for In-Situ Defects Detection in Additive Manufacturing.” In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 603–608. IEEE.
  • Chang, F., M. Dong, M. Liu, L. Wang, and Y. Duan. 2020. “A Lightweight Appearance Quality Assessment System Based on Parallel Deep Learning for Painted Car Body.” IEEE Transactions on Instrumentation and Measurement 69 (8): 5298–5307. https://doi.org/10.1109/TIM.19.
  • Chen, W., H. Lin, Y. Zhai, K. Luo, Y. Ma, and J. Li. 2019. “The Classification Detection Technol. of Car Quarter Glass Based on the Feature Parameter.” In 2019 Chinese Automation Congress, 4454–4458. IEEE.
  • Chen, Y., G. Wang, and Q. Fu. 2022. “Surface Defect Detection Method Based on Improved Attention Mechanism and Feature Fusion Model.” Computational Intelligence and Neuroscience 2022: Article id 3188645. https://doi.org/10.1155/2022/3188645.
  • Chu, Y., D. Feng, Z. Liu, L. Zhang, Z. Zhao, Z. Wang, Z. Feng, and X.-G. Xia. 2022. “A Fine-grained Attention Model for High Accuracy Operational Robot Guidance.” IEEE Internet of Things Journal 10 (2): 1066–1081. https://doi.org/10.1109/JIOT.2022.3206388.
  • Chung, Y.-H., and Y.-L. Chen. 2022. “Three-dimensional Image Inpainting System Using 3D-ED-GAN for Efficient Vision-based Detection for Rotor Dynamic Balance System.” IEEE Access 10:60025–60038. https://doi.org/10.1109/ACCESS.2022.3180339.
  • Cioffi, R., M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice. 2020. “Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Direct.” Sustainability 12 (2): 492. https://doi.org/10.3390/su12020492.
  • Dai, W., D. Li, D. Tang, H. Wang, and Y. Peng. 2022. “Deep Learning Approach for Defective Spot Welds Classification Using Small and Class-imbalanced Datasets.” Neurocomputing 477:46–60. https://doi.org/10.1016/j.neucom.2022.01.004.
  • Davies, E. R. 2004. Machine Vision: Theory, Algorithms, Practicalities. San Francisco, CA: Elsevier.
  • Da Xu, L., W. He, and S. Li. 2014. “Internet of Things in Industries: A Survey.” IEEE Transactions on Industrial Informatics 10 (4): 2233–2243. https://doi.org/10.1109/TII.2014.2300753.
  • Díaz-Romero, D., W. Sterkens, S. Van den Eynde, T. Goedemé, W. Dewulf, and J. Peeters. 2021. “Deep Learning Computer Vision for the Separation of Cast-and Wrought-aluminum Scrap.” Resources, Conservation and Recycling 172:105685. https://doi.org/10.1016/j.resconrec.2021.105685.
  • Do, A. T., Q. -C. Hsu, and F. -C. Tang. 2017. “Study on Measurement System for Non-Uniform Diameter Spring by Machine Vision.” In 2017 International Conference on System Science and Engineering (ICSSE), 253. IEEE.
  • Du, W., H. Shen, J. Fu, G. Zhang, and Q. He. 2019. “Approaches for Improvement of the X-ray Image Defect Detection of Automobile Casting Aluminum Parts Based on Deep Learning.” NDT & E International 107:102144. https://doi.org/10.1016/j.ndteint.2019.102144.
  • Evangelista, D., M. Antonelli, A. Pretto, C. Eitzinger, M. Moro, C. Ferrari, and E. Menegatti. 2020. “Spirit-a Software Framework for the Efficient Setup of Industrial Inspection Robots.” In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, 622–626. IEEE.
  • Fan, J., P. Zheng, and S. Li. 2022. “Vision-based Holistic Scene Understanding Towards Proactive Human–robot Collaboration.” Robotics and Computer-Integrated Manufacturing 75:102304. https://doi.org/10.1016/j.rcim.2021.102304.
  • Farooq, S., and C. O'Brien. 2012. “A Technology Selection Framework for Integrating Manufacturing Within a Supply Chain.” International Journal of Production Research 50 (11): 2987–3010. https://doi.org/10.1080/00207543.2011.588265.
  • Feldmann, S., G. Kempter, R. Esslinger, and H. T. Tran. 2020. “Support of Image-Based Quality Assessment in Discrete Production Scenarios Through AI-Based Decision Support.” In Proceedings of the 2020 4th International Conference on Algorithms, Computing and Systems, 92–97.
  • Ferguson, M., R. Ak, Y.-T. T. Lee, and K. H. Law. 2017. “Automatic Localization of Casting Defects with Convolutional Neural Networks.” In 2017 IEEE International Conference on Big Data (Big Data), 1726–1735. IEEE.
  • Fernández, A., and R. Méndez-Rial. 2020. “Embedded Vision System for Monitoring Arc Welding with Thermal Imaging and Deep Learning.” In 2020 International Conference on Omni-Layer Intelligent Systems (COINS), 1–6. IEEE.
  • Ferreira, L. A., M. Á. Souto, D. Fernández, M. Carmody, and J. Cebreiros. 2019. “Smart System for Calibration of Automotive Racks in Logistics 4.0 Based on Cad Environment.” In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 536–543. IEEE.
  • Frank, D., J. Chhor, and R. Schmitt. 2017. “Stereo-Vision for Autonomous Industrial Inspection Robots.” In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2555–2561. IEEE.
  • Gallagher, K., A. Hatch, and M. Munro. 2008. “Software Architecture Visualization: An Evaluation Framework and Its Application.” IEEE Transactions on Software Engineering 34 (2): 260–270. https://doi.org/10.1109/TSE.2007.70757.
  • Gove, R., and J. Uzdzinski. 2013. “A Performance-based System Maturity Assessment Framework.” Procedia Computer Science 16:688–697. https://doi.org/10.1016/j.procs.2013.01.072.
  • Häcker, J., F. Engelhardt, and D. D. Freyw. 2002. “Robust Manufacturing Inspection and Classification with Machine Vision.” International Journal of Production Research 40 (6): 1319–1334. https://doi.org/10.1080/00207540110116309.
  • Haleem, A., and M. Javaid. 2019. “Additive Manufacturing Applications in Industry 4.0: a Review.” Journal of Industrial Integration and Management 4 (4): 1930001. https://doi.org/10.1142/S2424862219300011.
  • Hatch, K. G. A., and M. Munro. 2008. “A Framework for Software Architecture Visualisation Assessment.”
  • Heger, J., and M. Z. El Abdine. 2019. “Using Data Mining Techniques to Investigate the Correlation Between Surface Cracks and Flange Lengths in Deep Drawn Sheet Metals.” IFAC-PapersOnLine 52 (13): 851–856. https://doi.org/10.1016/j.ifacol.2019.11.236.
  • Hein-Pensel, F., H. Winkler, A. Brückner, M. Wölke, I. Jabs, I. J. Mayan, A. Kirschenbaum, J. Friedrich, and C. Zinke-Wehlmann. 2023. “Maturity Assessment for Industry 5.0: A Review of Existing Maturity Models.” Journal of Manufacturing Systems 66:200–210. https://doi.org/10.1016/j.jmsy.2022.12.009.
  • Holst, C., T. B. Yavuz, P. Gupta, P. Ganser, and T. Bergs. 2022. “Deep Learning and Rule-based Image Processing Pipeline for Automated Metal Cutting Tool Wear Detection and Measurement.” IFAC-PapersOnLine 55 (2): 534–539. https://doi.org/10.1016/j.ifacol.2022.04.249.
  • Hongjuan, Y., M. Decai, and Z. Yunchu. 2021. “Preprocessing of Automobile Engine Connecting Rod Based on Shadow Removal and Image Enhancement.” In 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), 428–432. IEEE.
  • Hornáček, M., H. Küffner-McCauley, M. Trimmel, P. Rupprecht, and S. Schlund. 2022. “A Spatial Ar System for Wide-area Axis-aligned Metric Augmentation of Planar Scenes.” CIRP Journal of Manufacturing Science and Technology 37:219–226. https://doi.org/10.1016/j.cirpj.2022.01.011.
  • Hu, F.. 2022. “Mutual Information-enhanced Digital Twin Promotes Vision-guided Robotic Grasping.” Advanced Engineering Informatics 52:101562. https://doi.org/10.1016/j.aei.2022.101562.
  • Ivanov, M., A. Ulanov, and N. Cherkasov. 2022. “Visual Control of Weld Defects Using Computer Vision System on FANUC Robot.” In 2022 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 859–863. IEEE.
  • Jaber, M., and S. Goyal. 2009. “A Basic Model for Co-ordinating a Four-level Supply Chain of a Product with a Vendor, Multiple Buyers and Tier-1 and Tier-2 Suppliers.” International Journal of Production Research 47 (13): 3691–3704. https://doi.org/10.1080/00207540701805604.
  • Jiang, T., H. Cui, X. Cheng, and W. Tian. 2020. “A Measurement Method for Robot Peg-in-hole Prealignment Based on Combined Two-level Visual Sensors.” IEEE Transactions on Instrumentation and Measurement 70:1–12. https://doi.org/10.1109/TIM.19.
  • JiWei, O., L. C. TAY, and W. K. LAI. 2019. “Bottom-Hat Filtering for Defect Detection with CNN Classification on Car Wiper Arm.” In 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), 90–95. IEEE.
  • Kansizoglou, I., L. Bampis, and A. Gasteratos. 2021. “Deep Feature Space: A Geometrical Perspective.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10): 6823–6838. https://doi.org/10.1109/TPAMI.2021.3094625.
  • Kansizoglou, I., L. Bampis, and A. Gasteratos. 2022. “Do Neural Network Weights Account for Classes Centers?.” IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2022.3153134.
  • Kansizoglou, I., E. Misirlis, K. Tsintotas, and A. Gasteratos. 2022. “Continuous Emotion Recognition for Long-term Behavior Modeling Through Recurrent Neural Networks.” Technologies 10 (3): 59. https://doi.org/10.3390/technologies10030059.
  • Katika, T., F. K. Konstantinidis, T. Papaioannou, A. Dadoukis, S. N. Bolierakis, G. Tsimiklis, and A. Amditis. 2022. “Exploiting Mixed Reality in a Next-Generation IoT Ecosystem of a Construction Site.” In 2022 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Kaushik, S., A. Jain, T. Chaudhary, and N. Chauhan. 2022. “Machine Vision Based Automated Inspection Approach for Clutch Friction Disc (CFD).” Materials Today: Proceedings 62:151–157.
  • Keele, S. 2007. “Guidelines for Performing Systematic Literature Reviews in Software Engineering.” Technical report, Technical report, ver. 2.3 ebse technical report. ebse.
  • Konstantinidis, F. K., V. Balaska, S. Symeonidis, S. G. Mouroutsos, and A. Gasteratos. 2022. “Arowa: An Autonomous Robot Framework for Warehouse 4.0 Health and Safety Inspection Operations.” In 2022 30th Mediterranean Conference on Control and Automation (MED), 494–499. IEEE.
  • Konstantinidis, F. K., V. Balaska, S. Symeonidis, F. Psarommatis, A. Psomoulis, G. Giakos, S. G. Mouroutsos, and A. Gasteratos. 2023. “Achieving Zero Defected Products in Diary 4.0 Using Digital Twin and Machine Vision.” In Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, 528–534.
  • Konstantinidis, F. K., V. Balaska, S. Symeonidis, D. Tsilis, S. G. Mouroutsos, L. Bampis, A. Psomoulis, and A. Gasteratos. 2023. “Automating Dairy Production Lines with the Yoghurt Cups Recognition and Detection Process in the Industry 4.0 Era.” Procedia Computer Science 217:918–927. https://doi.org/10.1016/j.procs.2022.12.289.
  • Konstantinidis, F. K., A. Gasteratos, and S. G. Mouroutsos. 2018. “Vision-Based Product Tracking Method for Cyber-Physical Production Systems in Industry 4.0.” In 2018 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Konstantinidis, F. K., I. Kansizoglou, N. Santavas, S. G. Mouroutsos, and A. Gasteratos. 2020. “Marma: A Mobile Augmented Reality Maintenance Assistant for Fast-track Repair Procedures in the Context of Industry 4.0.” Machines 8 (4): 88. https://doi.org/10.3390/machines8040088.
  • Konstantinidis, F. K., I. Kansizoglou, K. A. Tsintotas, S. G. Mouroutsos, and A. Gasteratos. 2021. The Role of Machine Vision in Industry 4.0: A Textile Manufacturing Perspective.” In 2021 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Konstantinidis, F. K., S. G. Mouroutsos, and A. Gasteratos. 2021. The Role of Machine Vision in Industry 4.0: An Automotive Manufacturing Perspective.” In 2021 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Konstantinidis, F. K., N. Myrillas, S. G. Mouroutsos, D. Koulouriotis, and A. Gasteratos. 2022. “Assessment of Industry 4.0 for Modern Manufacturing Ecosystem: A Systematic Survey of Surveys.” Machines 10 (9): 746. https://doi.org/10.3390/machines10090746.
  • Konstantinidis, F. K., S. Sifnaios, G. Tsimiklis, S. G. Mouroutsos, A. Amditis, and A. Gasteratos. 2023. “Multi-sensor Cyber-physical Sorting System (CPSS) Based on Industry 4.0 Principles: A Multi-functional Approach.” Procedia Computer Science217:227–237. https://doi.org/10.1016/j.procs.2022.12.218.
  • Kumar, P., D. Singh, and J. Bhamu. 2022. “Machine Vision in Industry 4.0: Applications, Challenges and Future Directions.” In Machine Vision for Industry 4.0, edited by R. Raut, S. Krit, and P. Chatterjee, 263–284. Boca Raton, FL: CRC Press.
  • Li, J., R. Du, J. Zhang, J. Zhu, H. Xu, and M. Cai. 2021. “Autofeeding System for Assembling the CBCS on Automobile Engine Based on 3-D Vision Guidance.” IEEE Transactions on Instrumentation and Measurement 70:1–13.
  • Li, D., S. Hua, Z. Li, X. Gong, and J. Wang. 2022. “Automatic Vision-based Online Inspection System for Broken-filament of Carbon Fiber with Multiscale Feature Learning.” IEEE Transactions on Instrumentation and Measurement 71:1–12.
  • Li, X., L. Wang, and N. Cai. 2004. “Machine-vision-based Surface Finish Inspection for Cutting Tool Replacement in Production.” International Journal of Production Research 42 (11): 2279–2287. https://doi.org/10.1080/0020754042000197702.
  • Li, Q., B. Yang, S. Wang, Z. Zhang, X. Tang, and C. Zhao. 2022. “A Fine-grained Flexible Graph Convolution Network for Visual Inspection of Resistance Spot Welds Using Cross-domain Features.” Journal of Manufacturing Processes 78:319–329. https://doi.org/10.1016/j.jmapro.2022.04.025.
  • Liberati, A., D. G. Altman, J. Tetzlaff, C. Mulrow, P. C. Gøtzsche, J. P. Ioannidis, M. Clarke, et al. 2009. “The PRISMA Statement for Reporting Systematic Reviews and Meta-analyses of Studies that Evaluate Health Care Interventions: Explanation and Elaboration.” Journal of Clinical Epidemiology 62 (10): e1–e34. https://doi.org/10.1016/j.jclinepi.2009.06.006.
  • Lin, J., C. Lin, L. Pan, Z. Chen, J. Yu, and Y. Chen. 2019. “Design of Intelligent Detection System for Artificial Installation of Automobile Parts.” In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, Vol. 1, 862. IEEE.
  • Lin, T.-C., K. J. Wang, and M. L. Sheng. 2020. “To Assess Smart Manufacturing Readiness by Maturity Model: A Case Study on Taiwan Enterprises.” International Journal of Computer Integrated Manufacturing 33 (1): 102–115. https://doi.org/10.1080/0951192X.2019.1699255.
  • Liu, Z., J. Geng, X. Dai, T. Swierzewski, and K. Shimada. 2022. “Robotic Depowdering for Additive Manufacturing Via Pose Tracking.” IEEE Robotics and Automation Letters 7 (4): 10770–10777. https://doi.org/10.1109/LRA.2022.3195189.
  • Liu, K., Y. Qiao, H. Yang, Y. Li, and W. Yu. 2019. “Research on Construction Method of Glue Spreading Quality Database Based on Extreme Learning Machine.” In 2019 Chinese Control And Decision Conference (CCDC), 5675–5680. IEEE.
  • Liu, Y., J. Wang, H. Yu, J. Li, F. Li, and Q. Zhao. 2022. “A Non-Invasive System for On-Line Surface Defect Detection on Special-Shaped Steel Towards Real Production Lines.” In 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 1–6. IEEE.
  • Lor, K. H., and K. M. Goh. 2019. “Car Wiper Arm Defect Detection Using Gabor Filter.” In 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 164–169. IEEE.
  • Lu, Q., M. Grasso, T.-P. Le, and M. Seita. 2022. “Predicting Build Density in L-PBF Through in-situ Analysis of Surface Topography Using Powder Bed Scanner Technology.” Additive Manufacturing51:102626. https://doi.org/10.1016/j.addma.2022.102626.
  • Luo, Q., X. Fang, L. Liu, C. Yang, and Y. Sun. 2020. “Automated Visual Defect Detection for Flat Steel Surface: A Survey.” IEEE Transactions on Instrumentation and Measurement 69 (3): 626–644. https://doi.org/10.1109/TIM.19.
  • Luo, Q., X. Fang, J. Su, J. Zhou, B. Zhou, C. Yang, L. Liu, W. Gui, and L. Tian. 2020. “Automated Visual Defect Classification for Flat Steel Surface: A Survey.” IEEE Transactions on Instrumentation and Measurement 69 (12): 9329–9349. https://doi.org/10.1109/TIM.19.
  • Mangold, S., C. Steiner, M. Friedmann, and J. Fleischer. 2022. “Vision-based Screw Head Detection for Automated Disassembly for Remanufacturing.” Procedia CIRP 105:1–6. https://doi.org/10.1016/j.procir.2022.02.001.
  • Massaro, A., I. Manfredonia, A. Galiano, and N. Contuzzi. 2019. “Inline Image Vision Technique for Tires Industry 4.0: Quality and Defect Monitoring in Tires Assembly.” In 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT), 54–57. IEEE.
  • Mazzetto, M., L. F. Southier, M. Teixeira, and D. Casanova. 2019. “Automatic Classification of Multiple Objects in Automotive Assembly Line.” In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 363–369. IEEE.
  • M'baya, A., J. Laval, and N. Moalla. 2017. “An Assessment Conceptual Framework for the Modernization of Legacy Systems.” In 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 1–11. IEEE.
  • Miao, L., C. Han, B. Zhang, and X. Qi. 2019. “Research on the Wheel Model Automatic Identification System.” In 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 329–332. IEEE.
  • Miao, Y., J. Y. Jeon, and G. Park. 2020. “An Image Processing-based Crack Detection Technique for Pressed Panel Products.” Journal of Manufacturing Systems 57:287–297. https://doi.org/10.1016/j.jmsy.2020.10.004.
  • Mittal, S., M. A. Khan, D. Romero, and T. Wuest. 2018. “A Critical Review of Smart Manufacturing & Industry 4.0 Maturity Models: Implications for Small and Medium-sized Enterprises (SMEs).” Journal of Manufacturing Systems 49:194–214. https://doi.org/10.1016/j.jmsy.2018.10.005.
  • Mohammadikaji, M., S. Bergmann, S. Irgenfried, J. Beyerer, C. Dachsbacher, and H. Wörn. 2017. “Probabilistic Surface Inference for Industrial Inspection Planning.” In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 1008–1016. IEEE.
  • Molina, J., J. E. Solanes, L. Arnal, and J. Tornero. 2017. “On the Detection of Defects on Specular Car Body Surfaces.” Robotics and Computer-Integrated Manufacturing 48:263–278. https://doi.org/10.1016/j.rcim.2017.04.009.
  • Mora, M., G. Forgionne, J. Gupta, F. Cervantes, and O. Gelman. 2003. “A Framework to Assess Intelligent Decision-Making Support Systems.” In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 59–65. Springer.
  • Moradi, N., S. G. Kandi, and H. Yahyaei. 2022. “A New Approach for Detecting and Grading Blistering Defect of Coatings Using a Machine Vision System.” Measurement 203:111954. https://doi.org/10.1016/j.measurement.2022.111954.
  • Mourtzis, D.. 2020. “Simulation in the Design and Operation of Manufacturing Systems: State of the Art and New Trends.” International Journal of Production Research 58 (7): 1927–1949. https://doi.org/10.1080/00207543.2019.1636321.
  • Mourtzis, D., J. Angelopoulos, and N. Panopoulos. 2022. “A Literature Review of the Challenges and Opportunities of the Transition From Industry 4.0 to Society 5.0.” Energies 15 (17): 6276. https://doi.org/10.3390/en15176276.
  • Mourtzis, D., J. Angelopoulos, and N. Panopoulos. 2023. “The Future of the Human–machine Interface (HMI) in Society 5.0.” Future Internet 15 (5): 162. https://doi.org/10.3390/fi15050162.
  • Müller, J. 2020. “Enabling Technologies for Industry 5.0.” European Commission 8–10.
  • Mulrow, C. D.. 1994. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309 (6954): 597–599. https://doi.org/10.1136/bmj.309.6954.597.
  • Nguyen, H. G., R. Habiboglu, and J. Franke. 2022. “Enabling Deep Learning Using Synthetic Data: A Case Study for the Automotive Wiring Harness Manufacturing.” Procedia CIRP 107:1263–1268. https://doi.org/10.1016/j.procir.2022.05.142.
  • Novakovic, B. 2017. “Design of An Adaptable Tooling System for Part to Part Variation Processing.”
  • Ooi, J. 2019. “Bottom-Hat Filtering for Defect Detection with CNN Classification on Car Wiper Arm.” PhD thesis, Tunku Abdul Rahman University College.
  • Oztemel, E., and S. Gursev. 2020. “Literature Review of Industry 4.0 and Related Technologies.” Journal of Intelligent Manufacturing 31 (1): 127–182. https://doi.org/10.1007/s10845-018-1433-8.
  • Page, M. J., J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, et al. 2021. “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews.” Systematic Reviews 10 (1): 1–11. https://doi.org/10.1186/s13643-020-01552-x.
  • Park, J., M. B. Jun, and H. Yun. 2022. “Development of Robotic Bin Picking Platform with Cluttered Objects Using Human Guidance and Convolutional Neural Network (CNN).” Journal of Manufacturing Systems 63:539–549. https://doi.org/10.1016/j.jmsy.2022.05.011.
  • Paszkiewicz, A., M. Salach, M. Ganzha, M. Paprzycki, M. Bolanowski, G. Budzik, H. Wójcik, F. Konstantinidis, and C. E. Palau. 2022. “Implementation of UI Methods and UX in VR in Case of 3D Printer Tutorial.” In New Trends in Intelligent Software Methodologies, Tools and Techniques, 460–471. IOS Press.
  • Pei, Z., and L. Chen. 2018. “Welding Component Identification and Solder Joint Inspection of Automobile Door Panel Based on Machine Vision.” In 2018 Chinese Control And Decision Conference (CCDC), 6558–6563. IEEE.
  • Peters, S., G. Lanza, N. Jun, J. Xiaoning, Y. Pei Yun, and M. Colledani. 2014. “Automotive Manufacturing Technologies – An International Viewpoint.”
  • Piero, N., and M. Schmitt. 2017. “Virtual Commissioning of Camera-based Quality Assurance Systems for Mixed Model Assembly Lines.” Procedia Manufacturing 11:914–921. https://doi.org/10.1016/j.promfg.2017.07.195.
  • Posilović, L., D. Medak, M. Subašić, T. Petković, M. Budimir, and S. Lončarić. 2021. “Synthetic 3D Ultrasonic Scan Generation Using Optical Flow and Generative Adversarial Networks.” In 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), 213–218. IEEE.
  • Psarommatis, F., and D. Kiritsis. 2022. “A Hybrid Decision Support System for Automating Decision Making in the Event of Defects in the Era of Zero Defect Manufacturing.” Journal of Industrial Information Integration 26:100263. https://doi.org/10.1016/j.jii.2021.100263.
  • Psarommatis, F., and G. May. 2022. “A Standardized Approach for Measuring the Performance and Flexibility of Digital Twins.” International Journal of Production Research 63: 1–16.
  • Psarommatis, F., and G. May. 2023. “A Literature Review and Design Methodology for Digital Twins in the Era of Zero Defect Manufacturing.” International Journal of Production Research 61 (16): 5723–5743. https://doi.org/10.1080/00207543.2022.2101960.
  • Psarommatis, F., G. May, and V. Azamfirei. 2023. “Envisioning Maintenance 5.0: Insights From a Systematic Literature Review of Industry 4.0 and a Proposed Framework.” Journal of Manufacturing Systems 68:376–399. https://doi.org/10.1016/j.jmsy.2023.04.009.
  • Psarommatis, F., G. May, P.-A. Dreyfus, and D. Kiritsis. 2020. “Zero Defect Manufacturing: State-of-the-art Review, Shortcomings and Future Directions in Research.” International Journal of Production Research 58 (1): 1–17. https://doi.org/10.1080/00207543.2019.1605228.
  • Psarommatis, F., S. Prouvost, G. May, and D. Kiritsis. 2020. “Product Quality Improvement Policies in Industry 4.0: Characteristics, Enabling Factors, Barriers, and Evolution Toward Zero Defect Manufacturing.” Frontiers in Computer Science 2:26. https://doi.org/10.3389/fcomp.2020.00026.
  • Psarommatis, F., J. Sousa, J. P. Mendonça, and D. Kiritsis. 2022. “Zero-defect Manufacturing the Approach for Higher Manufacturing Sustainability in the Era of Industry 4.0: a Position Paper.” International Journal of Production Research 60 (1): 73–91. https://doi.org/10.1080/00207543.2021.1987551.
  • Putz, V., M. Stangl, C. Kohlberger, and R. Naderer. 2019. “Computer Vision Approach for the Automated Tool Alignment of An Orbital Sanding Robot.” IFAC-PapersOnLine 52 (15): 19–24. https://doi.org/10.1016/j.ifacol.2019.11.643.
  • Rahimi, A., M. Anvaripour, and K. Hayat. 2021. “Object Detection Using Deep Learning in a Manufacturing Plant to Improve Manual Inspection.” In 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–7. IEEE.
  • Răileanu, S., T. Borangiu, F. Anton, and S. Anton. 2021. “Open Source Machine Vision Platform for Manufacturing and Robotics.” IFAC-PapersOnLine 54 (1): 522–527. https://doi.org/10.1016/j.ifacol.2021.08.060.
  • Ranft, B., and C. Stiller. 2016. “The Role of Machine Vision for Intelligent Vehicles.” IEEE Transactions on Intelligent Vehicles 1 (1): 8–19. https://doi.org/10.1109/TIV.2016.2551553.
  • Rocha, C. A. P., E. Rauch, T. Vaimel, M. A. R. Garcia, and R. Vidoni. 2021. “Implementation of a Vision-based Worker Assistance System in Assembly: A Case Study.” Procedia CIRP 96:295–300. https://doi.org/10.1016/j.procir.2021.01.090.
  • Saif, Y., Y. Yusof, K. Latif, A. Z. A. Kadir, A. Adam, N. Hatem, and D. A. Memon. 2022. “Roundness Holes' Measurement for Milled Workpiece Using Machine Vision Inspection System Based on IoT Structure: A Case Study.” Measurement 195:111072. https://doi.org/10.1016/j.measurement.2022.111072.
  • Sanz, J. L. 2012. Advances in Machine Vision. Springer Science & Business Media.
  • Schlüter, M., C. Niebuhr, J. Lehr, and J. Krüger. 2018. “Vision-based Identification Service for Remanufacturing Sorting.” Procedia Manufacturing 21:384–391. https://doi.org/10.1016/j.promfg.2018.02.135.
  • Semeniuta, O., S. Dransfeld, and P. Falkman. 2016. “Vision-Based Robotic System for Picking and Inspection of Small Automotive Components.” In 2016 IEEE International Conference on Automation Science and Engineering (CASE), 549–554. IEEE.
  • Semeniuta, O., S. Dransfeld, K. Martinsen, and P. Falkman. 2018. “Towards Increased Intelligence and Automatic Improvement in Industrial Vision Systems.” Procedia CIRP 67:256–261. https://doi.org/10.1016/j.procir.2017.12.209.
  • Shafique, S., S. Abid, F. Riaz, and Z. Ejaz. 2021. “Computer Vision Based Autonomous Navigation in Controlled Environment.” In 2021 International Conference on Robotics and Automation in Industry (ICRAI), 1–6. IEEE.
  • Shan, Z., M. Xin, and W. Di. 2021. “Measuring Method of Involute Profile Error Based on Machine Vision.” In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), 1272–1276. IEEE.
  • Silva, R. L., O. Canciglieri Junior, and M. Rudek. 2022. “A Road Map for Planning-deploying Machine Vision Artifacts in the Context of Industry 4.0.” Journal of Industrial and Production Engineering 39 (3): 167–180. https://doi.org/10.1080/21681015.2021.1965665.
  • Silva, R. L., M. Rudek, A. L. Szejka, and O. C. Junior. 2018. “Machine Vision Systems for Industrial Quality Control Inspections.” In IFIP International Conference on Product Lifecycle Management, 631–641. Springer.
  • Smith, M. L., L. N. Smith, and M. F. Hansen. 2021. “The Quiet Revolution in Machine Vision-a State-of-the-art Survey Paper, Including Historical Review, Perspectives, and Future Directions.” Computers in Industry 130:103472. https://doi.org/10.1016/j.compind.2021.103472.
  • Snyder, H.. 2019. “Literature Review As a Research Methodology: An Overview and Guidelines.” Journal of Business Research 104:333–339. https://doi.org/10.1016/j.jbusres.2019.07.039.
  • Sohal, A. S., J. Sarros, R. Schroder, and P. O'neill. 2006. “Adoption Framework for Advanced Manufacturing Technologies.” International Journal of Production Research 44 (24): 5225–5246. https://doi.org/10.1080/00207540600558320.
  • Soini, A. 2001. “Machine Vision Technology Take-Up in Industrial Applications.” In ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat., 332–338. IEEE.
  • Tandiya, A., S. Akthar, M. Moussa, and C. Tarray. 2018. “Automotive Semi-Specular Surface Defect Detection System.” In 2018 15th Conference on Computer and Robot Vision (CRV), 285–291. IEEE.
  • Tao, X., X. Gong, X. Zhang, S. Yan, and C. Adak. 2022. “Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey.” IEEE Transactions on Instrumentation and Measurement 71: 1–21. https://doi.org/10.1109/TIM.2022.3196436.
  • Tao, F., H. Zhang, A. Liu, and A. Y. Nee. 2018. “Digital Twin in Industry: State-of-the-art.” IEEE Transactions on Industrial Informatics 15 (4): 2405–2415. https://doi.org/10.1109/TII.9424.
  • Tirmizi, A., B. De Cat, K. Janssen, Y. Pane, P. Leconte, and M. Witters. 2019. “User-Friendly Programming of Flexible Assembly Applications with Collaborative Robots.” In 2019 20th International Conference on Research and Education in Mechatronics (REM), 1–7. IEEE.
  • Tranfield, D., D. Denyer, and P. Smart. 2003. “Towards a Methodology for Developing Evidence-informed Management Knowledge by Means of Systematic Review.” British Journal of Management 14 (3): 207–222. https://doi.org/10.1111/bjom.2003.14.issue-3.
  • Trotta, D., and P. Garengo. 2019. “Assessing Industry 4.0 Maturity: An Essential Scale for SMEs.” In 2019 8th International Conference on Industrial Technology and Management (ICITM), 69–74. IEEE.
  • Tsai, D.-M., and M.-F. Chen. 1996. “A Fast Machine Vision Approach for Automatic Recognition of Industrial Parts.” International Journal of Production Research 34 (3): 687–699. https://doi.org/10.1080/00207549608904928.
  • Tsintotas, K. A., S. An, I. T. Papapetros, F. K. Konstantinidis, G. C. Sirakoulis, and A. Gasteratos. 2022. “Dimensionality Reduction Through Visual Data Resampling for Low-Storage Loop-Closure Detection.” In 2022 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Tsintotas, K. A., L. Bampis, and A. Gasteratos. 2018a. “Assigning Visual Words to Places for Loop Closure Detection.” In 2018 IEEE International Conference on Robotics and Automation (ICRA), 5979–5985. IEEE.
  • Tsintotas, K. A., L. Bampis, and A. Gasteratos. 2018b. “Doseqslam: Dynamic On-Line Sequence Based Loop Closure Detection Algorithm for SLAM.” In 2018 IEEE International Conference on Imaging Systems and Techniques (IST), 1–6. IEEE.
  • Tsintotas, K. A., L. Bampis, and A. Gasteratos. 2021. “Modest-vocabulary Loop-closure Detection with Incremental Bag of Tracked Words.” Robotics and Autonomous Systems 141:103782. https://doi.org/10.1016/j.robot.2021.103782.
  • Tsintotas, K. A., L. Bampis, A. Gasteratos. 2021. “Tracking-doseqslam: A Dynamic Sequence-based Visual Place Recognition Paradigm.” IET Computer Vision 15 (4): 258–273. https://doi.org/10.1049/cvi2.v15.4.
  • Tsintotas, K. A., L. Bampis, and A. Gasteratos. 2022a. Online Appearance-Based Place Recognition and Mapping: Their Role in Autonomous Navigation. Vol. 133. Cham: Springer Nature.
  • Tsintotas, K. A., L. Bampis, and A. Gasteratos. 2022b. “The Revisiting Problem in Simultaneous Localization and Mapping.” In Online Appearance-Based Place Recognition and Mapping: Their Role in Autonomous Navigation, 1–33. Cham: Springer Nature.
  • Tsintotas, K. A., L. Bampis, and A. Gasteratos. 2022c. “The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection.” IEEE Transactions on Intelligent Transportation Systems 23 (11): 19929–19953. https://doi.org/10.1109/TITS.2022.3175656.
  • Turner, E., L. Newberry, S. Santinga, J. Gray, S. Gopu, J. Peoples, J. Hobbs, and S. White. 2019. Applying Computer Vision to Track Tool Movement in an Automotive Assembly Plant.” In Proceedings of the 2019 ACM Southeast Conference, 214–217.
  • Tušar, T., K. Gantar, V. Koblar, B. Ženko, and B. Filipič. 2017. “A Study of Overfitting in Optimization of a Manufacturing Quality Control Procedure.” Applied Soft Computing 59:77–87. https://doi.org/10.1016/j.asoc.2017.05.027.
  • Velasco, L. S. F., P. E. R. Revilla, L. V. R. Rodríguez, M. P. Santa Hincapié, L. A. Saavedra-Robinson, and J. -F. Jiménez. 2022. “A Human-centred Workstation in Industry 4.0 for Balancing the Industrial Productivity and Human Well-being.” International Journal of Industrial Ergonomics 91:103355. https://doi.org/10.1016/j.ergon.2022.103355.
  • Viharos, F. Z. J., S. D. Chetverikov, T. A. Háry, F. R. Sághegyi, F. A. Barta, S. L. Zalányi, S. I. Pomozi, et al. 2016. “Vision Based, Statistical Learning System for Fault Recognition in Industrial Assembly Environment.” In 2016 21st International Conference on Emerging Technologies and Factory Automation. IEEE.
  • Vitolo, F., P. Franciosa, D. Ceglarek, S. Patalano, and M. De Martino. 2019. “A Generalised Multi-Attribute Task Sequencing Approach for Robotics Optical Inspection Systems.” In 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0 IoT), 117–122.
  • Wang, C.-C., B. C. Jiang, Y.-S. Chou, and C.-C. Chu. 2011. “Multivariate Analysis-based Image Enhancement Model for Machine Vision Inspection.” International Journal of Production Research 49 (10): 2999–3021. https://doi.org/10.1080/00207541003801242.
  • Wang, W., Y. Luo, K. Yang, and C. Shang. 2019. “Multi-angle Automotive Fuse Box Detection and Assembly Method Based on Machine Vision.” Measurement 145:234–243. https://doi.org/10.1016/j.measurement.2019.05.100.
  • Wang, J., Y. Song, C. Yuan, F. Guo, Y. Huangfu, and Y. Liu. 2022. “Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era.” Computational Intelligence and Neuroscience 2022. https://doi.org/10.1155/2022/9230412.
  • Weckx, S., S. Robyns, J. Baake, E. Kikken, R. De Geest, M. Birem, and D. Maes. 2022. “A Cloud-based Digital Twin for Monitoring of An Adaptive Clamping Mechanism Used for High Performance Composite Machining.” Procedia Computer Science 200:227–236. https://doi.org/10.1016/j.procs.2022.01.221.
  • Wei, D., S. An, X. Zhang, J. Tian, K. A. Tsintotas, A. Gasteratos, and H. Zhu. 2022. “Dual Regression for Efficient Hand Pose Estimation.” In 2022 International Conference on Robotics and Automation (ICRA), 6423–6429. IEEE.
  • Wolfschläger, D., J.-H. Woltersmann, B. Montavon, and R. H. Schmitt. 2022. “Sheared Edge Defect Segmentation Using a Convolutional U-net for Quantified Quality Assessment of Fine Blanked Workpieces.” Precision Engineering 75:129–141. https://doi.org/10.1016/j.precisioneng.2022.01.010.
  • Würschinger, H., M. Mühlbauer, M. Winter, M. Engelbrecht, and N. Hanenkamp. 2020. “Implementation and Potentials of a Machine Vision System in a Series Production Using Deep Learning and Low-cost Hardware.” Procedia CIRP 90:611–616. https://doi.org/10.1016/j.procir.2020.01.121.
  • Yan, X., C. Chen, and X. Li. 2022. “Adaptive Vision-Based Control of Redundant Robots with Null-Space Interaction for Human–Robot Collaboration.” In 2022 International Conference on Robotics and Automation (ICRA), 2803–2809. IEEE.
  • Yi, S., S. Liu, X. Xu, X. V. Wang, S. Yan, and L. Wang. 2022. “A Vision-based Human-robot Collaborative System for Digital Twin.” Procedia CIRP 107:552–557. https://doi.org/10.1016/j.procir.2022.05.024.
  • Zhang, Y., S. Shan, F. D. Frumosu, M. Calaon, W. Yang, Y. Liu, and H. N. Hansen. 2022. “Automated Vision-based Inspection of Mould and Part Quality in Soft Tooling Injection Moulding Using Imaging and Deep Learning.” CIRP Annals 71 (1): 429–432. https://doi.org/10.1016/j.cirp.2022.04.022.
  • Zheng, C., Y. An, Z. Wang, H. Wu, X. Qin, B. Eynard, and Y. Zhang. 2022. “Hybrid Offline Programming Method for Robotic Welding Systems.” Robotics and Computer-Integrated Manufacturing 73:102238. https://doi.org/10.1016/j.rcim.2021.102238.
  • Zheng, B., Y. Li, J. Zhang, C. Wen, S. Zhu, and D. Xu. 2022. “Detection of Piston Surface Defects Based on Machine Vision.” In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Vol. 6, 852–857. IEEE.
  • Zhou, Z., L. Li, A. Fürsterling, H. J. Durocher, J. Mouridsen, and X. Zhang. 2022. “Learning-based Object Detection and Localization for a Mobile Robot Manipulator in SME Production.” Robotics and Computer-Integrated Manufacturing 73:102229. https://doi.org/10.1016/j.rcim.2021.102229.

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