Hệ thống Vật lý-Xã hội cho các Hoạt động Lắp ráp Micro-/Nano: Một Bảng Tóm tắt

Current Robotics Reports - Tập 2 Số 1 - Trang 33-41 - 2021
Jose A. Mulet Alberola1, Irene Fassi1
1Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (CNR), 20133, Milan, Italy

Tóm tắt

Tóm tắt Mục đích của Bài đánh giá Các yêu cầu mới nhất của thị trường toàn cầu buộc các hệ thống sản xuất phải thay đổi để phù hợp với một mô hình sản xuất mới (Công nghiệp 4.0). Các Hệ thống Vật lý-Xã hội (CPS) xuất hiện như một giải pháp được triển khai trong nhiều lĩnh vực sản xuất khác nhau, đặc biệt là trong những lĩnh vực có giá trị gia tăng cao và độ phức tạp công nghệ, có nhiều biến thể sản phẩm và thời gian đưa ra thị trường ngắn. Trong nghĩa này, bài báo này nhằm đánh giá mức độ giới thiệu công nghệ CPS trong sản xuất vi mô/nano và cách mà những công nghệ này có thể ứng phó với những yêu cầu sản xuất đầy thách thức này. Các phát hiện gần đây Sự giới thiệu của CPS vẫn còn ở giai đoạn đầu trên nhiều ứng dụng công nghiệp, nhưng thực tế cho thấy tiềm năng của nó để hỗ trợ mô hình sản xuất trong tương lai. Tuy nhiên, chỉ có một vài công trình nghiên cứu trong lĩnh vực sản xuất vi mô/nano xem xét các khung CPS, vì khái niệm này chỉ mới xuất hiện cách đây khoảng một thập kỷ. Tóm tắt Một số đóng góp đã chỉ ra tiềm năng của công nghệ CPS trong việc cải thiện hiệu suất sản xuất mà có thể được mở rộng cho sản xuất vi mô/nano. Các khung dựa trên IoT với công nghệ VR/AR cho phép hệ thống phân tán và hợp tác, hoặc kiến trúc dựa trên tác nhân với những triển khai thuật toán tiên tiến giúp cải thiện tính linh hoạt và hiệu suất các hoạt động lắp ráp vi mô/nano. Các nghiên cứu tương lai về CPS trong các hoạt động lắp ráp vi mô/nano nên tiếp tục với nhiều nghiên cứu hơn về việc triển khai công nghệ của nó, cho thấy các tác động của nó từ những góc độ khác nhau, tức là góc nhìn bền vững, kinh tế và xã hội, để khai thác toàn bộ tính năng của nó.

Từ khóa


Tài liệu tham khảo

Boudet J, Gregg B, Rathje K, Stein E, Vollhardt K McKinsey & Company. The future of personalization—and how to get ready for it. [Online].; 2019 [cited 2020 September. Available from: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-future-of-personalization-and-how-to-get-ready-for-it.

Zhou K, Liu T, Zhou L. Industry 4.0: towards future industrial opportunities and challenges. In: In 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). Zhangjiajie; 2015. https://doi.org/10.1109/FSKD.2015.7382284.

Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering. 2017;3(5):616–30. https://doi.org/10.1016/J.ENG.2017.05.015.

Sanderson D, Chaplin JC, de Silva L, Holmes P, Ratchev S. Smart manufacturing and reconfigurable technologies: towards an integrated environment for evolvable assembly systems. In: In IEEE 1st International Workshops on Foundations and Applications of Self-* Systems. Augsburg; 2016. https://doi.org/10.1109/FAS-W.2016.61.

Pfrommer J, Stogl D, Aleksandrov K, Navarro SE, Hein B, Beyerer J. Plug & produce by modelling skills and service-oriented orchestration of reconfigurable manufacturing systems. At-Automatisierungstechnik. 2015;63(10):790–800. https://doi.org/10.1515/auto-2014-1157.

Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, et al. Cyber-physical systems in manufacturing. CIRP Ann Manuf Technol. 2016;65:621–41. https://doi.org/10.1016/j.cirp.2016.06.005.

Monostori L. Cyber-physical production systems: roots, expectations and R&D challenges. In: CIRP P, editor. editor 47th CIRP Conference on Manufacturing Systems, vol. 17. Ontario: Windsor; 2014. p. 9–13. https://doi.org/10.1016/j.procir.2014.03.115.

Patel R, Hedelind M, Lozan-Villegas P. Enabling robots in small-part assembly lines: the “ROSETTA approach” - an industrial perspective. In: In ROBOTIK. Munich: 7th German Conference on Robotics; 2012. p. 279.

Hedelind M, Kock S. Requirements on flexible robot systems for small parts assembly: a case study. In: In IEEE International Symposium on Assembly and Manufacturing (ISAM). Tampere; 2011. https://doi.org/10.1109/ISAM.2011.5942356.

Kock S, Vittor T, Matthias B, Jerregard H, Kälman M, Lundberg I. Robot concept for scalable, flexible assembly automation: a technology study on a harmless dual-armed robot. In: In IEEE International Symposium on Assembly and Manufacturing (ISAM). Tampere; 2011. https://doi.org/10.1109/ISAM.2011.5942358.

MarketsandMarkets. Industry 4.0 market by technology (IoT, artificial intelligence, industrial metrology, industrial robotics, AR & VR, blockchain, 3D printing, digital twin, and 5G – offering, application, and end users) and geography- Global Forecast to 2024. ; 2019. https://www.marketsandmarkets.com/Market-Reports/industry-4-market-102536746.html.

Gendreau D, Gauthier M, Hériban D, Lutz P. Modular architecture of the microfactories for automatic micro-assembly. Robotics and Computer-Intergrated Manufacturing. 2010;26:354–60. https://doi.org/10.1016/j.rcim.2009.11.013.

Papo DO, Stepjanou HE. Micro and mesoscale robotic assembly. J Manuf Process. 2004;6(1):–71. https://doi.org/10.1016/S1526-6125(04)70059-6.

Banerjee AG, Gupta SK. Research in automated planning and control for micromanipulation. IEEE Trans Autom Sci Eng. 2013;10, 10(3):–495. https://doi.org/10.1109/TASE.2013.2260539.

Dejeu J, Bechelany M, Rougeot P, Philippe L, Gauthier M. Adhesion control for micro- and nanomanipulation. ACS Nano. 2011;5(6):4648–57. https://doi.org/10.1021/nn200658z.

Gauthier M, Regnier S, Rougeot P, Chaillet N. Analysis of forces for micromanipulations in dry and liquid media. J Micromech. 2006;3(3–4):389–413. https://doi.org/10.1163/156856306777924699.

Jain RK, Majumder S, Ghosh B, Saha S. Analysis of multiple robotic assemblies by cooperation of multimobile micromanipulation systems (M4S). International journal of Adnvanced Manufacturing Technology. 2017;91:9–12. https://doi.org/10.1007/s00170-016-9969-2.

Gunda R, Cecil J, Calyam P, Kak S. Information centric frameworks for micro assembly. In On the Move to Meaningful Internet Systems: Workshop. 2011. https://doi.org/10.1007/978-3-642-25126-9_17.

Rasheed A, San O, Kvamsdal T. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access. 2020;8:21980–2012. https://doi.org/10.1109/ACCESS.2020.2970143.

Grieves M, Vickers J. Digital twin: mitigating unpredictable, undesirable emergent behavios in complex systems. In Kahlen FJ, Flumerfelt S, Alves AC. Transdisciplinary perspectives on complex systems: new findings and approaches.:2017.85–113. https://doi.org/10.1007/978-3-319-38756-7_4.

Dobra Z, Dhir KS. Technology jump in the industry: human-robot cooperation in production. Ind Robot. 2020;47(5):757–75.

Gajsek B, Stradovnik S, Hace A. Sustainable move towards flexible, robotic, human-involving workplace. Sustainability. 2020;12:6590. https://doi.org/10.3390/su12166590.

Faccio M, Minto R, Rosati G, Bottin M. The influence of the product characteristics on human-robot collaboration: a model for the performance of collaborative robotic assembly. The International Journal of Adnvance Manufacturing Technology. 2019;106:2317–31. https://doi.org/10.1007/s00170-019-04670-6.

Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M. Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics. 2018;14:14(11)–4734. https://doi.org/10.1109/TII.2018.2852491.

Monostori L, Váncza J, Kumara SR. Agent-based systems for manufacturing. CIRP Ann Manuf Technol. 2006;55(2):697–720. https://doi.org/10.1016/j.cirp.2006.10.004.

Andreadis G, Klazaglou P, Niotaki K, Bouzakis KD. Classification and review of multi-agents systems in the manufacturing section. Procedia Engineering. 2014;69:282–90. https://doi.org/10.1016/j.proeng.2014.02.233.

Fontana G, Ruggeri S, Fassi I, Legnani G. A mini work-cell for handling and assembling microcomponents. Assem Autom. 2014;34(1):27–33. https://doi.org/10.1108/AA-11-2012-087.

Wei Y, Xu Q. An overview of micro-force sensing techniques. Sensors Actuators A Phys. 2015;123:359–74. https://doi.org/10.1016/j.sna.2015.09.028.

Cappelleri DJ, Adam G. Towards a real-time 3d vision-based micro-force sensing probe. Journal of Micro-Bio Robotics. 2020;16:23–32. https://doi.org/10.1007/s12213-019-00122-2.

Jing W, Chowdhury S, Guix M, Wang J, An Z, Johnson BV, et al. A microforce-sensing mobile microrobot for automated micromanipulation tasks. IEEE Trans Autom Sci Eng. 2019;16, 16(2):–530. https://doi.org/10.1109/TASE.2018.2833810.

Zhao Y, Huang X, Liu Y, Wang G, Hong K. Design and control of a piezoelectric-driven microgripper perceiving displacement and gripping force. Micromachines. 2020;11(2):121. https://doi.org/10.3390/mi11020121.

Jain RK, Majumder S, Ghosh B, Saha S. Design and manufacturing of mobile micro manipulation system with a compliant piezoelectric actuator based micro gripper. J Manuf Syst. 2015;35:76–91. https://doi.org/10.1016/j.jmsy.2014.12.001.

Hamdi M, Ferreira A. Microassembly planning using physical-based models in virtual environment. In: In IEEE/RSJ International Conference on Intelligent Robotics and Systems. Sendai; 2004.

Wang P, Xu Q. Design and modelling of constant-force mechanism: a survey. Mech Mach Theory. 2018;119:1–21. https://doi.org/10.1016/j.mechmachtheory.2017.08.017.

Kleeberger K, Bormann R, Kraus W, Huber MF. A survey on learning-based robotic grasping. Current Robotics Reports. 2020;1:239–49. https://doi.org/10.1007/s43154-020-00021-6.

Bafuma Liseli J, Dahmouche R, Kumar P, Seon JA, Gauthier M. Enhancing in-hand dexterous micro-manipulation for real-time applications. In: In International Conference on Automation Science and Engineering (CASE). Munich; 2018. https://doi.org/10.1109/COASE.2018.8560362.

• Seon JA, Dahmouche R, Gauthier M. Enhance in-hand dexterous micromanipulation by exploiting adhesion forces. IEEE Transactions on Robotics. 2018;34(1):113–25. https://doi.org/10.1109/TRO.2017.2765668The results of this work highlight the role of adhesion forces during the manipulation of micro-objects and how advanced control systems may exploit these physical interactions for process optimization.

van Vuuren JJ, Tang L, Al-Bahadly I, Arif K. Towards the autonomous robotic gripping and handling of novel objects. In: In 14th IEEE Conference on Industrial Electronics and Applications (ICIEA). Xi’an; 2019. https://doi.org/10.1109/ICIEA.2019.8833640.

Bodenhagen L, Fugl AR, Willatzen M, Petersen HG, Krüger N. Learning peg-in-hole actions with flexible objects. In: In 4th International Conference on Agents and Artificial Intelligence (ICAART). Vilamoura; 2012.

Xing D, Xu D, Li H, Luo L. Active calibration and its applications on micro-operating platform with multiple manipulators. In: In IEEE International Conference on Robotics & Automation (ICRA). Hong Kong; 2014. https://doi.org/10.1109/ICRA.2014.6907661.

Shao C, Ye X, Qian J, Zhang Z, Zhu D. Robotic precision assembly system for microstructures. Journal of Systems and Control Engineering. 2020;234(8):948–58. https://doi.org/10.1177/0959651819885755.

Fontana G, Ruggeri S, Legnani G, Fassi I. Unconventional calibration strategies for micromanipulation work-cells. Robotica. 2018;36(12):1897–919. https://doi.org/10.1017/S0263574718000796.

Zhang JY, Xu SP, Liu Y, Hao YP. Research on the identification method of micro assembly part. In: In 2nd Conference on Image, Vision and Computing (ICIVC). Chengdu; 2017. https://doi.org/10.1109/ICIVC.2017.7984564.

Tamadazte B, Le Fort-Piat N, Dembélé S, Fortier G. Robotic micromanipulation for microassembly: modelling by sequential function chart and achievement by multiple scale visual servoings. Journal of Micro-Nano Mechatronics. 2009;5:1–12.

Brahim T, Marchand E, Dembélé S, Le For-Piat N. CAD model-based tracking and 3D visual-based control for MEMS microassembly. The International Journal of Robotics Research. 2010;11(29):1416–34. https://doi.org/10.1177/0278364910376033.

Yesin KB, Nelson BJ. A CAD model based tracking system for visually guided microassembly. Robotica. 2005;23:409–18. https://doi.org/10.1017/S0263574704000840.

Venkatesan V, Seymour J, Cappelleri DJ. Microassembly sequence and path planning using sub-assemblies. Journal of Mechanisms and Robotics. 2018;10(6). https://doi.org/10.1115/1.4041333.

Venkatesan V, Cappelleri DJ. Path planning and micromanipulation using a learned model. IEEE Robotics and Automation Letters. 2018;3(4):3096. https://doi.org/10.1109/LRA.2018.2849568.

Kim E, Kojima M, Mae Y, Arai T. High-speed manipulation of microobjects using an automated two-fingered microhand for 3d microassembly. Micromachines. 2020;11:534. https://doi.org/10.3390/mi11050534.

Hsu A, Zhao H, Gaudreault M, Foy AW, Pelrine R. Magnetic milli-robot swarm platform: a safety barrier certificate enabled, low-cost test bed. IEEE Robotics and Automation Letters. 2020;5(2):2920. https://doi.org/10.1109/LRA.2020.2974713.

Shuang B, Chen J, Li Z. Microrobot based micro-assembly sequence planning with hybrid and colony algorithm. Internation Journal on Manufacturing Technology. 2008;38:1227–35. https://doi.org/10.1007/s00170-007-1165-y.

Wang L, Gao RX, Váncza J, Krüger J, Wang XV, Makris S, et al. Symbiotic human-robot collaborative assembly. CIRP Ann. 2019;68(2):701–26. https://doi.org/10.1016/j.cirp.2019.05.002.

Haddadin S, Croft E. Physical human-robot interaction. In Siciliano B, Khatib O. Springer handbook of robotics.:2016.1835–1874. https://doi.org/10.1007/978-3-319-32552-1_69.

Mutlu B, Roy N, Sabanovic S. Cognitive human-robot interaction. In Siciliano B, Khatib O. Springer Handbook of Robotics.:2016.1907–1934. https://doi.org/10.1007/978-3-319-32552-1_71.

Bilberg A, Malik AA. Digital twin driven human-robot collaborative assembly. CIRP Ann Manuf Technol. 2019;68:499–502. https://doi.org/10.1016/j.cirp.2019.04.011.

Ammi M, Ferreira A, Fontaine JG. Virtualized reality interface for tele-micromanipulation. In: In IEEE International Conference on Robotics & Automation (ICRA). New Orleans; 2004. https://doi.org/10.1109/robot.2004.1307481.

Probst M, Hürzeler C, Borer R, Nelson BJ. Virtual Reality for microassembly. In Proceedings of SPIE - The International Society for Optical Engineering. 2007. https://doi.org/10.1117/12.754557.

Cassier C, Ferreira A, Hirai S. Combination of vision servoing techniques and VR-based simulation for semi-autonomous microassembly workstation. In: In IEEE International Conference on Robotics & Automation (ICRA). Washington, D.C.; 2002. p. 1501–6. https://doi.org/10.1109/ROBOT.2002.1014756.

Cecil J, Huber J, Gobinath N, Jacques J. A virtual factory environment to support process design in micro assembly domains. Computer-Aided Design & Applications. 2011;8(1):119–27. https://doi.org/10.3722/cadaps.2011.119-127.

Cecil J, Jones J. VREM: An advanced virtual environment for micro assembly. Int J Adv Manuf Technol. 2014;72(1–4):47–56. https://doi.org/10.1007/s00170-014-5618-9.

Chiou R, Kwon Y. Remotely adjustable robotic grip force for the network-based assembly automation. Internation Journal on Advance Manufacturing technology. 2011;54:1145–54. https://doi.org/10.1007/s00170-010-2979-6.

Mehrtash M, Khamesee MB, Tarao S, Tsuda N, Chang JY. Human-assisted virtual reality for a magnetic-haptic micromanipulation platform. Microsystems Technology. 2012;18(9–10):1407–15. https://doi.org/10.1007/s00542-012-1560-7.

Kojima O, Huang S, Marakami K, Ishikawa M, Yamakawa Y. Human–robot interaction system for micromanipulation assistance. In: In IECON - 44th Annual Conference of the IEEE Industrial Electronics Society. Washington D.C.; 2018. https://doi.org/10.1109/IECON.2018.8592819.

Chang RJ, Jau JC. Error measurement and calibration in developing virtual-reality-assisted microassembly system. International Journal of Automation Technology. 2015;9(6). https://doi.org/10.20965/ijat.2015.p0619.

Chang RJ, Jau JC. Augmented reality in peg-in-hole microassembly operation. International Journal of Automation Technology. 2016;10(3):438–46. https://doi.org/10.20965/ijat.2016.p0438.

Onori M, Barata J, Frei R. Evolvable assembly systems: basic principles. International Federation for Information Processing (IFIP). 2006;220:317–28.

Onori M, Semere D, Lindberg B. Evolvable systems: an approach to self-X production. Int J Comput Integr Manuf. 2011;24(5):506–16.

Hofmann A, Bretthauer G, Siltala N, Tuokko R. Evolvable Micro Production Systems: specific needs and differences to macro. In: In IEEE International Symposium on Assembly and Manufacturing (ISAM). Tampere; 2011. p. 1–6. https://doi.org/10.1109/ISAM.2011.5942361.

Järvenpää E, Heikkilä R, Tuokko R. TUT-microfactory – a small-size, modular and sustainable production. In: In 11th Global Conference on Sustainable Manufacturing. Berlin; 2013.

Hofmann A, Hummel B, Firat O, Bretthauer G, Bär M, Meyer M. microFLEX - a new concept to address the needs for adaptable meso and micro assembly lines. In: In IEEE International Symposium on Assembly and Manufacturing (ISAM). Tampere; 2011. https://doi.org/10.1109/ISAM.2011.5942297.

•• Cecil J, Albuhamood S, Cecil-Xavier A, Ramanathan P. An advanced cyber physical framework for micro devices assembly. IEEE Transactions on Systems Manufacturing and Cybernetics. 2019;49(1):92–106. https://doi.org/10.1109/TSMC.2017.2733542This work discusses a major advance in manufacturing by the deployment of a collaborative demonstrator and shows the potential of CPS for micro/nano-manufacturing assembly operations.

•• Xuemin S, Jinsong B, Yiming ZS, Bin Z. A digital twin-driven approach for the assembly-commissioning of high precision products. Robotics and Computer Integrated Manufacturing. 2020:61. https://doi.org/10.1016/j.rcim.2019.101839This complete work represents a state-of-the-art example of how CPS can be implemented and how this framework allows the analysis and optimization of the entire assembly process.

Ferreira P, Lohse N, Ratchev S. Multi-agent architecture for reconfiguration of precision modular assembly systems. In: In International Precision Assembly Seminar (IPAS). Chamonix; 2010. https://doi.org/10.1007/978-3-642-11598-1_29.

Weyer S, Schmitt M, Ohmer M, Gorecky D. Towards Industry 4.0 - standardization as the cruzial challenge for highly modular, multi-vendor production systems. 15th IFAC Symposium on Information Control Problems in Manufacturing (INCOM). 2015;48(3):579–l.