螺絲鎖附在機械裝配及工業組裝生產線上扮演著關鍵角色，以不適當的工具角度進行鎖附作業，將影響產品品質，或是降低生產效率。此外，在高度彈性及高度客製化的組裝生產線中，多數仍須倚賴人力進行作業；同時，為了保留必要的生產彈性及因應客製化衍伸的多樣性，作業的複雜性及多變程度大幅提升，也增加了作業人員對於組裝作業的認知需求和降低了學習曲線，由此可見輔助作業系統的重要及必要性。基於上述，本研究提出一套智慧型鎖附作業導引系統，能於鎖附作業過程中，同時監督鎖附工具角度及識別鎖附順序。此系統可即時地針對不符合規範的動作提出警示，且依實驗結果顯示，前述兩項功能分別有94%及91%的準確率。因此可期待此模型有助於導正錯誤動作，提供作業指引，提升生產效率及產品品質。

Screw fastening plays a crucial role in mechanical assembly and industrial production lines. Performing fastening operations with inappropriate tool angles can affect product quality or reduce production efficiency. Furthermore, in highly flexible and customized assembly lines, most operations still rely on human labor. Meanwhile, to maintain necessary production flexibility and accommodate the diversity stemming from customization, the complexity and variability of operations have significantly increased. This raises the cognitive demands for operators and lowers the learning curve, highlighting the importance and necessity of an assistive operation system. Based on the above, this study proposes an intelligent fastening assistant system (IFAS) that can supervise the fastening tool angle and fastening sequence during the process. IFAS can issue real-time alerts for incorrect actions, and according to experimental results, the aforementioned two functions have an accuracy rate of 94% and 91%, respectively. Therefore, it is expected that this study will help correct erroneous actions, provide operation guidance, and improve production efficiency and product quality.

Aehnelt, M., & Bader, S. (2015, January). Information Assistance for Smart Assembly Stations. In ICAART (2) (pp. 143-150).
Alharbi, R. H., Alshaye, M. B., Alkanhal, M. M., Alharbi, N. M., Alzahrani, M. A., & Alrehaili, O. A. (2020, March). Deep learning based algorithm for automatic scoliosis angle measurement. In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE.
Baek, K., Bang, J., Lee, J., Choi, M., & So, H. (2022). 3D Printing-Assisted Soft Capacitive Inclinometers for Simultaneous Monitoring of Tilt Angles and Directions. IEEE Access, 10, 31445-31454.
Bahramgiri, M., Nooshabadi, S., Olutomilayo, K. T., & Fuhrmann, D. R. (2022). Hitch Angle Estimation for Trailer Backup System—An Object Detection and Tracking Approach. IEEE Transactions on Instrumentation and Measurement, 71, 1-15.
Baumeister, J., Ssin, S. Y., ElSayed, N. A., Dorrian, J., Webb, D. P., Walsh, J. A., ... & Thomas, B. H. (2017). Cognitive cost of using augmented reality displays. IEEE transactions on visualization and computer graphics, 23(11), 2378-2388.
Besginow, A., Büttner, S., Ukita, N., & Röcker, C. (2022). Deep learning-based action detection for continuous quality control in interactive assistance systems. In Human-Technology Interaction: Shaping the Future of Industrial User Interfaces (pp. 127-149). Cham: Springer International Publishing.
Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
Dalle Mura, M., Dini, G., & Failli, F. (2016). An integrated environment based on augmented reality and sensing device for manual assembly workstations. Procedia CIRP, 41, 340-345.
Faccio, M., Ferrari, E., Galizia, F. G., Gamberi, M., & Pilati, F. (2019). Real-time assistance to manual assembly through depth camera and visual feedback. Procedia CIRP, 81, 1254-1259.
Faccio, M., Ferrari, E., Gamberi, M., & Pilati, F. (2019). Human Factor Analyser for work measurement of manual manufacturing and assembly processes. The International Journal of Advanced Manufacturing Technology, 103, 861-877.
Fischer, C., Lušić, M., Faltus, F., Hornfeck, R., & Franke, J. (2016). Enabling live data controlled manual assembly processes by worker information system and nearfield localization system. Procedia CIRP, 55, 242-247.
Fukuda, K., Yamanobe, N., Ramirez-Alpizar, I. G., & Harada, K. (2020, January). Assembly motion recognition framework using only images. In 2020 IEEE/SICE International Symposium on System Integration (SII) (pp. 1242-1247). IEEE.
Heydariaan, M., Dabirian, H., & Gnawali, O. (2020, May). Anguloc: Concurrent angle of arrival estimation for indoor localization with uwb radios. In 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 112-119). IEEE.
Hinrichsen, S., & Bendzioch, S. (2019). How digital assistance systems improve work productivity in assembly. In Advances in Human Factors and Systems Interaction: Proceedings of the AHFE 2018 International Conference on Human Factors and Systems Interaction, July 21-25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA 9 (pp. 332-342). Springer International Publishing.
Hinrichsen, S., Riediger, D., & Unrau, A. (2016, September). Assistance systems in manual assembly. In Proceedings of 6th International conference on Production Engineering and Management (Vol. 29, pp. 3-14).
Kita, N., & Kato, T. (2022, December). Image Measurement Method for Automatic Insertion of Forks into Inclined Pallet. In 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 441-448). IEEE.
Lai, Z. H., Tao, W., Leu, M. C., & Yin, Z. (2020). Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing. Journal of Manufacturing Systems, 55, 69-81.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing.
Mark, B. G., Rauch, E., & Matt, D. T. (2020). Study of the impact of projection-based assistance systems for improving the learning curve in assembly processes. Procedia CIRP, 88, 98-103.
Mark, B. G., Rauch, E., & Matt, D. T. (2021). Worker assistance systems in manufacturing: A review of the state of the art and future directions. Journal of Manufacturing Systems, 59, 228-250.
Matsumoto, T., Doyo, D., Shida, K., & Kanazawa, T. (2011). Development and implementation of a skill transfer system for a self-tapping screw-tightening operation. Industrial Engineering and Management Systems, 10(3), 209-220.
Meinel, D., Ehler, F., Lipka, M., & Franke, J. (2018). Supporting manual assembly through merging live position data and 3D-CAD data using a worker information system. In Tagungsband des 3. Kongresses Montage Handhabung Industrieroboter (pp. 187-194). Springer Berlin Heidelberg.
Mengoni, M., Ceccacci, S., Generosi, A., & Leopardi, A. (2018). Spatial Augmented Reality: An application for human work in smart manufacturing environment. Procedia Manufacturing, 17, 476-483.
Miao, R., Shan, Z., Zhou, Q., Wu, Y., Ge, L., Zhang, J., & Hu, H. (2022). Real-time defect identification of narrow overlap welds and application based on convolutional neural networks. Journal of Manufacturing Systems, 62, 800-810.
Moghaddam, F. B., Lopez, A. J., De Vuyst, S., & Gautama, S. (2021, April). Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories. In 2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 142-148). IEEE.
Obinata, T., Kawamoto, H., & Sankai, Y. (2020, October). Development of real-time assembly work monitoring system based on 3D skeletal model of arms and fingers. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 363-368). IEEE.
Petzoldt, C., Keiser, D., Beinke, T., & Freitag, M. (2020). Functionalities and Implementation of Future Informational Assistance Systems for Manual Assembly: Towards Individualized, Incentive-Based Assistance and Support of Ergonomics. In Subject-Oriented Business Process Management. The Digital Workplace–Nucleus of Transformation: 12th International Conference, S-BPM ONE 2020, Bremen, Germany, December 2-3, 2020, Proceedings 12 (pp. 88-109). Springer International Publishing.
Petzoldt, C., Keiser, D., Beinke, T., & Freitag, M. (2020). Requirements for an incentive-based assistance system for manual assembly. In Dynamics in Logistics: Proceedings of the 7th International Conference LDIC 2020, Bremen, Germany (pp. 541-553). Springer International Publishing.
Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
Sakamaki, R., & Horibe, M. (2020). Precision adjustment of probe-tilt angle with RF signal detection technique. IEEE Transactions on Instrumentation and Measurement, 69(10), 8500-8505.
Thakur, S., & Singh, S. (2022). Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals. Applied Acoustics, 201, 109126.
Wang K. J. and Yan Y. J. (2021) A smart operator assistance system using deep learning for angle measurement. IEEE Transactions on Instrumentation and Measurement, 70, 1-14.
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE.