Operator 4.0 Towards Human-centric Smart Manufacturing: Framework, Enabling Technologies and Typical Scenarios

doi:10.3901/JME.2022.18.251

Abstract

Abstract: Operator is the most flexible and initiative element in manufacturing systems. The emerging information technologies empower rapid improvements of efficiency and quality in manufacturing systems, while also enhancing operators with diverse capabilities in perception, perception and control. In human-centric smart manufacturing era, the traditional operators were unable to adapt to new scenes, technologies and problems, thus the new-generation operators (also called Operator 4.0) was proposed, studied and implemented. Based on operator functions in manufacturing system, the evolutions of operators' roles are analyzed from the perspectives of industrial revolutions. Also, the connotation of Operator 4.0 is analyzed in the context of human-cyber-physical systems. According to the task characteristics and technical scheme, Operator 4.0 can be divided into different types, including strength operator, cognitive enhancement operator, cooperative operator, etc. The reference architecture of Operator 4.0 is proposed from the perspectives of perceptive thread, cognitive thread and control thread. Key enabling technologies of Operator 4.0 are discussed, including exoskeleton, augmented reality, wearable technology, artificial intelligence, etc. Finally, the typical applications scenarios of Operator 4.0 are discussed, including augmented reality-aided complicated product assembly, industrial robot-aided manufacturing and virtual reality-aided manufacturing training, which reflect the application potential and effectiveness of Operator 4.0. This work is expected to provide references for the scientific research, technological development and practical applications of human-centric smart manufacturing.

Key words: intelligent manufacturing, human-centric smart manufacturing, Operator 4.0, human-cyber-physical systems

CLC Number:

HUANG Sihan, WANG Baicun, ZHANG Meidi, HUANG Jintang, ZHU Qizhang, YANG Geng. Operator 4.0 Towards Human-centric Smart Manufacturing: Framework, Enabling Technologies and Typical Scenarios[J]. Journal of Mechanical Engineering, 2022, 58(18): 251-264.

References

[1] 周济. 智能制造——“中国制造2025”的主攻方向[J].中国机械工程，2015，26(17):2273-2284. ZHOU Ji. Intelligent manufacturing:Main direction of “made in China 2025”[J]. China Mechanical Engineering，2015，26(17):2273-2284.
[2] ZHOU J，LI P，ZHOU Y，et al. Toward new-generation intelligent manufacturing[J]. Engineering，2018，4(1): 11-20.
[3] WANG B，TAO F，FANG X，et al. Smart manufacturing and intelligent manufacturing:a comparative review[J]. Engineering，2020，7(6):738-757.
[4] LI X，WANG B，LIU C，et al. Intelligent manufacturing systems in COVID-19 pandemic and beyond: framework and impact assessment[J]. Chinese Journal of Mechanical Engineering，2020，33(1):1-5.
[5] 臧冀原，王柏村，孟柳，等. 智能制造的三个基本范式:从数字化制造、“互联网+”制造到新一代智能制造[J]. 中国工程科学，2018，20(4):13-18.ZANG Jiyuan，WANG Baicun，MENG Liu，et al. Brief analysis on three basic paradigms of intelligent manufacturing[J]. Engineering Sciences，2018，20(4): 13-18.
[6] 王柏村，薛塬，延建林，等. 以人为本的智能制造:理念、技术与应用[J]. 中国工程科学，2020，22(4): 139-146.WANG Baicun，XUE Yuan，YAN Jianlin，et al. Human-centered intelligent manufacturing: overview and perspectives[J]. Strategic Study of CAE，2020，22(4):139-146.
[7] 王柏村，黄思翰，易兵，等. 面向智能制造的人因工程研究与发展[J]. 机械工程学报，2020，56(16):240-253.WANG Baicun，HUANG Sihan，YI Bing，et al. State-of-art of human factors/ergonomics in intelligent manufacturing[J]. Journal of Mechanical Engineering，2020，56(16):240-253.
[8] ZHOU J，ZHOU Y，WANG B，et al. Human-cyber-physical systems (HCPSs) in the context of new-generation intelligent manufacturing[J]. Engineering，2019，5(4):624-636.
[9] 王柏村，易兵，刘振宇，等. HCPS视角下智能制造的发展与研究[J]. 计算机集成制造系统，2021，27(10):2749-2761.WANG Baicun，YI Bing，LIU Zhenyu，et al. Evolution and state-of-the-art of intelligent manufacturing from HCPS perspective[J]. Computer Integrated Manufacturing Systems，2021，27(10):2749-2761.
[10] 王柏村，臧冀原，屈贤明，等. 基于人-信息-物理系统(HCPS)的新一代智能制造研究[J]. 中国工程科学，2018，20(4):29-34.WANG Baicun，ZANG Jiyuan，QU Xianming，et al. Research on new-generation intelligent manufacturing based on human-cyber-physical systems[J]. Engineering Sciences，2018，20(4):29-34.
[11] ROMERO D，BERNUS P，NORAN O，et al. The Operator 4.0: Human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems[C]//IFIP International Conference on Advances in Production Management Systems. Springer，Cham，2016: 677-686.
[12] ROMERO D，STAHRE J，WUEST T，et al. Towards an Operator 4.0 typology: A human-centric perspective on the fourth industrial revolution technologies[C]// Proceedings of the International Conference on Computers and Industrial Engineering (CIE46)，Tianjin，China. 2016:29-31.
[13] MATTSSON S，FAST-BERGLUND Å，LI D，et al. Forming a cognitive automation strategy for Operator 4.0 in complex assembly[J]. Computers & Industrial Engineering，2020，139:105360.
[14] SINGH S，TRETTEN P. Research anthology on cross-industry challenges of industry 4.0 IGI globbal[M].Hershey，USA:IGI Global，2021.
[15] BOUSDEKIS A，APOSTOLOU D，MENTZAS G. A human cyber physical system framework for Operator 4.0–artificial intelligence symbiosis[J]. Manufacturing Letters，2020，25:10-15.
[16] TAYLOR M P，BOXALL P，CHEN J J J，et al. Operator 4.0 or Maker 1.0? Exploring the implications of Industries 4.0 for innovation，safety and quality of work in small economies and enterprises[J]. Computers & Industrial Engineering，2020，139:105486.
[17] KAASINEN E，SCHMALFUSS F，ÖZTURK C，et al. Empowering and engaging industrial workers with Operator 4.0 solutions[J]. Computers & Industrial Engineering，2020，139:105678.
[18] ROSEN J，BRAND M，FUCHS M B，et al. A myosignal-based powered exoskeleton system[J]. IEEE Transactions on systems，Man，and Cybernetics-part A: Systems and Humans，2001，31(3):210-222.
[19] SYLLA N，BONNET V，COLLEDANI F，et al. Ergonomic contribution of ABLE exoskeleton in automotive industry[J]. International Journal of Industrial Ergonomics，2014，44(4):475-481.
[20] 史小华，王洪波，孙利，等. 外骨骼型下肢康复机器人结构设计与动力学分析[J]. 机械工程学报，2014，50(3):41-48.SHI Xiaohua，WANG Hongbo，SUN Li，et al. Design and dynamic analysis of an exoskeletal lower limbs rehabilitation robot[J]. Journal of Mechanical Engineering，2014，50(3):41-48.
[21] COLLINS S H，WIGGIN M B，SAWICKI G S. Reducing the energy cost of human walking using an unpowered exoskeleton[J]. Nature，2015，522(7555):212-215.
[22] MUNOZ L M. Ergonomics in the industry 4.0: exoskeletons[J]. J. Ergonomics，2018，8(1):176.
[23] 荆泓玮，朱延河，赵思恺，等. 外肢体机器人研究现状及发展趋势[J]. 机械工程学报，2020，56(7):1-9.JING Hongwei，ZHU Yanhe，ZHAO Sikai，et al. Research status and development trend of supernumerary robotic limbs[J]. Journal of Mechanical Engineering，2020，56(7):1-9.
[24] HUYSAMEN K，DE LOOZE M，BOSCH T，et al. Assessment of an active industrial exoskeleton to aid dynamic lifting and lowering manual handling tasks[J]. Applied ergonomics，2018，68:125-131.
[25] STADLER K S，ALTENBURGER R，SCHMIDHAUSER E，et al. Robo-mate an exoskeleton for industrial use—concept and mechanical design[C]//19th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines，London，UK. 2017:806-813.
[26] AZUMA R T. A survey of augmented reality[J]. Presence: Teleoperators & Virtual Environments，1997，6(4):355-385.
[27] CARMIGNIANI J，FURHT B. Handbook of augmented reality[M]. New York:Spriger，2011.
[28] BLANCO-NOVOA O，FERNANDEZ-CARAMES T M，FRAGA-LAMAS P，et al. A practical evaluation of commercial industrial augmented reality systems in an industry 4.0 shipyard[J]. IEEE Access，2018，6:8201-8218.
[29] DE PACE F，MANURI F，SANNA A. Augmented reality in industry 4.0[J]. American Journal of Computer Science and Information Technology，2018，6(1):1-7.
[30] PAELKE V. Augmented reality in the smart factory: supporting workers in an industry 4.0. environment[C]// Proceedings of the 2014 IEEE emerging technology and factory automation (ETFA). IEEE，2014:1-4.
[31] BRUNO F，BARBIERI L，MARINO E，et al. An augmented reality tool to detect and annotate design variations in an industry 4.0 approach[J]. The International Journal of Advanced Manufacturing Technology，2019，105(1):875-887.
[32] ZHENG J M，CHAN K W，GIBSON I. Virtual reality[J]. IEEE Potentials，1998，17(2):20-23.
[33] DAVIDSON J，FOWLER J，PANTAZIS C，et al. Integration of VR with BIM to facilitate real-time creation of bill of quantities during the design phase:A proof of concept study[J]. Frontiers of Engineering Management，2020，7(3):396-403.
[34] TURNER C J，HUTABARAT W，OYEKAN J，et al. Discrete event simulation and virtual reality use in industry: new opportunities and future trends[J]. IEEE Transactions on Human-Machine Systems，2016，46(6):882-894.
[35] MANCA D，BRAMBILLA S，COLOMBO S. Bridging between virtual reality and accident simulation for training of process-industry operators[J]. Advances in Engineering Software，2013，55:1-9.
[36] MUJBER T S，SZECSI T，HASHMI M S J. Virtual reality applications in manufacturing process simulation[J]. Journal of Materials Processing Technology，2004，155:1834-1838.
[37] MALIK A A，MASOOD T，BILBERG A. Virtual reality in manufacturing: Immersive and collaborative artificial- reality in design of human-robot workspace[J]. International Journal of Computer Integrated Manufacturing，2020，33(1):22-37.
[38] NNAJI C，AWOLUSI I. Critical success factors influencing wearable sensing device implementation in AEC industry[J]. Technology in Society，2021，66: 101636.
[39] MARDONOVA M，CHOI Y. Review of wearable device technology and its applications to the mining industry[J]. Energies，2018，11(3):547.
[40] FOXLIN E，NAIMARK L. VIS-Tracker: A wearable vision-inertial self-tracker[J]. VR，2003，3:199.
[41] LI J，LI H，UMER W，et al. Identification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology[J]. Automation in Construction，2020，109:103000.
[42] MAYBERRY A，HU P，MARLIN B，et al. Shadow: design of a wearable，real-time mobile gaze tracker[C]//Proceedings of the 12th Annual International Conference on Mobile systems，Applications，and Services. 2014:82-94.
[43] ZHOU Y，WANG L，DING L，et al. Intelligent technologies help operating mobile cabin hospitals effectively cope with COVID-19[J]. Frontiers of Engineering Management，2020，7(3):459-460.
[44] MYERS K，BERRY P，BLYTHE J，et al. An intelligent personal assistant for task and time management[J]. AI Magazine，2007，28(2):47-47.
[45] ROVEDA L，MASKANI J，FRANCESCHI P，et al. Model-based reinforcement learning variable impedance control for human-robot collaboration[J]. Journal of Intelligent & Robotic Systems，2020，100(2):417-433.
[46] ZOLOTOVÁ I，PAPCUN P，KAJÁTI E，et al. Smart and cognitive solutions for Operator 4.0: Laboratory H-CPPS case studies[J]. Computers & Industrial Engineering，2020，139:105471.
[47] MAURICE P，PADOIS V，MEASSON Y，et al. Human-oriented design of collaborative robots[J]. International Journal of Industrial Ergonomics，2017，57: 88-102.
[48] MICHALOS G，KOUSI N，KARAGIANNIS P，et al. Seamless human robot collaborative assembly-an automotive case study[J]. Mechatronics，2018，55: 194-211.
[49] FAGER P，CALZAVARA M，SGARBOSSA F. Modelling time efficiency of cobot-supported kit preparation[J]. The International Journal of Advanced Manufacturing Technology，2020，106:2227-2241.
[50] 姜杰文. 基于手势识别的协作机器人人机交互系统设计[D]. 大连:大连理工大学，2019.JIANG Jiewen. Design of human-computer interaction system for collaborative robot based on gesture recognition[D]. Dalian:Dalian University of Technology，2019.
[51] 张秀丽，谷小旭，赵洪福，等.一种基于串联弹性驱动器的柔顺机械臂设计[J]. 机器人，2016，38(4): 385-394.ZHANG Xiuli，GU Xiaoxu，ZHAO Hongfu，et al. Design of a compliant robotic arm based on series elastic actuator[J]. Robot，2016，38(4):385-394.
[52] MOURTZIS D，VLACHOU E，MILAS N. Industrial big data as a result of IoT adoption in manufacturing[J]. Procedia Cirp，2016，55:290-295.
[53] YANG F，WANG M. A review of systematic evaluation and improvement in the big data environment[J]. Frontiers of Engineering Management，2020，7(1): 27-46.
[54] 雷亚国，贾峰，周昕，等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报，2015，51(21):49-56.LEI Yaguo，JIA Feng，ZHOU Xin，et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering，2015，51(21):49-56.
[55] 廖小平，陈楷，鲁娟. 基于磨损监测保持切削加工表面质量稳定的实时控制研究[J]. 机械工程学报，2020，56(11):252-260.LIAO Xiaoping，CHEN Kai，LU Juan. Research on real-time control of machining surface quality stability based on wear monitoring[J]. Journal of Mechanical Engineering，2020，56(11):252-260.
[56] DRURY C G. Human factors/ergonomics implications of big data analytics:Chartered institute of ergonomics and human factors annual lecture[J]. Ergonomics，2015，58(5):659-673.
[57] 陶飞，刘蔚然，张萌，等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统，2019，25(1):1-18.TAO Fei，LIU Weiran，ZHANG Meng，et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems，2019，25(1):1-18.
[58] 李浩，刘根，文笑雨，等. 面向人机交互的数字孪生系统工业安全控制体系与关键技术[J]. 计算机集成制造系统，2021，27(2): 374-389.LI Hao，LIU Gen，WEN Xiaoyu，et al. Industrial safety control system and key technologies of digital twin system oriented to human-machine interaction[J]. Computer Integrated Manufacturing Systems，2021，27(2):374-389.
[59] HE F，ONG S K，NEE A Y C. An integrated mobile augmented reality digital twin monitoring system[J]. Computers，2021，10(8):99.
[60] WANG Q，JIAO W，WANG P，et al. Digital twin for human-robot interactive welding and welder behavior analysis[J]. IEEE/CAA Journal of Automatica Sinica，2020，8(2):334-343.
[61] WANG T，LI J，DENG Y，et al. Digital twin for human-machine interaction with convolutional neural network[J]. International Journal of Computer Integrated Manufacturing，2021:1-10.
[62] BILBERG A，MALIK A A. Digital twin driven human–robot collaborative assembly[J]. CIRP Annals，2019，68(1):499-502.
[63] 刘晓阳，刘恩福，靳江艳. 基于蚁群算法的异步并行装配序列规划方法[J]. 机械工程学报，2019，55(9): 107-119.LIU Xiaoyang，LIU Enfu，JIN Jiangyan. Asynchronous parallel assembly sequence planning based on ant colony algorithm[J]. Journal of Mechanical Engineering，2019，55(9):107-119.
[64] 武颖，姚丽亚，熊辉，等. 基于数字孪生技术的复杂产品装配过程质量管控方法[J]. 计算机集成制造系统，2019，25(6): 1568-1575.WU Ying，YAO Liya，XIONG Hui，et al. Quality control method of complex product assembly process based on digital twin technology[J]. Computer Integrated Manufacturing Systems，2019，25(6):1568-1575.
[65] WANG Z，BAI X，ZHANG S，et al. User-oriented AR assembly guideline:A new classification method of assembly instruction for user cognition[J]. The International Journal of Advanced Manufacturing Technology，2021，112(1):41-59.
[66] MILLER J，HOOVER M，WINER E. Mitigation of the Microsoft HoloLens’ hardware limitations for a controlled product assembly process[J]. The International Journal of Advanced Manufacturing Technology，2020，109(5):1741-1754.
[67] WANG X，ONG S K，NEE A Y C. A comprehensive survey of augmented reality assembly research[J]. Advances in Manufacturing，2016，4(1):1-22.
[68] ONG S K，PANG Y，NEE A Y C. Augmented reality aided assembly design and planning[J]. CIRP Annals，2007，56(1):49-52.
[69] 丁汉. 机器人与智能制造技术的发展思考[J]. 机器人技术与应用，2016(4):7-10.DING Han. Robot and intelligent manufacturing technology development thinking[J]. Robot Technique and Application，2016(4):7-10.
[70] 易怀安，刘坚，路恩会. 基于图像清晰度评价的磨削表面粗糙度检测方法[J]. 机械工程学报，2016，52(16): 15-21.YI Huaian，LIU Jian，LU Enhui. Detection method of grinding surface roughness based on image definition evaluation[J]. Journal of Mechanical Engineering，2016，52(16):15-21.
[71] 王田苗，陶永. 我国工业机器人技术现状与产业化发展战略[J]. 机械工程学报，2014，5(9):1-13.WANG Tianmiao，TAO Yong. Research status and industrialization development strategy of chinese industrial robot[J]. Journal of Mechanical Engineering，2014，5(9):1-13.
[72] BARBOSA W S，GIOIA M M，NATIVIDADE V G，et al. Industry 4.0: Examples of the use of the robotic arm for digital manufacturing processes[J]. International Journal on Interactive Design and Manufacturing (IJIDeM)，2020，14(4):1569-1575.
[73] HUBER A，WEISS A. Developing human-robot interaction for an industry 4.0 robot: how industry workers helped to improve remote-HRI to physical-HRI[C]//Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. 2017:137-138.
[74] KHALID A，KIRISCI P，GHRAIRI Z，et al. Towards implementing safety and security concepts for human-robot collaboration in the context of industry 4.0[C]//39th International MATADOR Conference on Advanced Manufacturing (Manchester，UK). 2017: 0-7.
[75] GONZALEZ A G C，ALVES M V S，VIANA G S，et al. Supervisory control-based navigation architecture: a new framework for autonomous robots in industry 4.0 environments[J]. IEEE Transactions on Industrial Informatics，2017，14(4):1732-1743.
[76] LIAGKOU V，SALMAS D，STYLIOS C. Realizing virtual reality learning environment for industry 4.0[J]. Procedia CIRP，2019，79:712-717.
[77] 蔡宝，朱文华，孙张驰，等. 虚拟现实技术在铣削加工实训教学中的应用[J]. 实验技术与管理，2020，37(1):137-140.CAI Bao，ZHU Wenhua，SUN Zhangchi，et al. Application of virtual reality technology in milling practical training teaching[J]. Experimental Technology and Management，2020，37(1):137-140.
[78] SALAH B，ABIDI M H，MIAN S H，et al. Virtual reality-based engineering education to enhance manufacturing sustainability in industry 4.0[J]. Sustainability，2019，11(5):1477.
[79] ROLDÁN J J，CRESPO E，MARTÍN-BARRIO A，et al. A training system for industry 4.0 operators in complex assemblies based on virtual reality and process mining[J]. Robotics and Computer-integrated Manufacturing，2019，59:305-316.
[80] SCHROEDER H，FRIEDEWALD A，KAHLEFENDT C，et al. Virtual reality for the training of operators in industry 4.0[C]//IFIP International Conference on Advances in Production Management Systems. Springer，Cham，2017:330-337.