Reconfigurable manufacturing systems: Principles, design, and future trends

doi:10.1007/s11465-018-0483-0

Front. Mech. Eng.

2018, Vol. 13

Issue (2) : 121-136 https://doi.org/10.1007/s11465-018-0483-0

REVIEW ARTICLE

Reconfigurable manufacturing systems: Principles, design, and future trends

Yoram KOREN¹(

), Xi GU¹, Weihong GUO²

¹. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
². Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA

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Abstract

Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are cost-effective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

Keywords reconfigurable manufacturing systems responsiveness intelligent manufacturing

Corresponding Author(s): Yoram KOREN

Just Accepted Date: 25 September 2017 Online First Date: 09 November 2017 Issue Date: 16 March 2018

Cite this article:

Yoram KOREN,Xi GU,Weihong GUO. Reconfigurable manufacturing systems: Principles, design, and future trends[J]. Front. Mech. Eng., 2018, 13(2): 121-136.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-018-0483-0
https://academic.hep.com.cn/fme/EN/Y2018/V13/I2/121

Fig.1 Manufacturing inventions initiated in Michigan

Tab.1 Core characteristics of RMS

Fig.2 Reconfiguration machine tool (RMT) and reconfigurable inspection machine (RIM) developed at the ERC-RMS. (a) RMT; (b) RIM

Fig.3 Ford Winsor Engine Plant with CNC machines [²⁶]

Fig.4 Schematic layout of (a) SLP configuration and (b) RMS configuration

Fig.5 A futuristic RMS configuration with RIMs and a return conveyor

Fig.6 A conceptual layout of a reconfigurable assembly system

1	Koren Y. Computer Control of Manufacturing Systems. New York: McGraw Hill, 1983
2	Koren Y, Heisel U, Jovane F, et al.Reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology, 1999, 48(2): 527–540 https://doi.org/10.1016/S0007-8506(07)63232-6
3	Parsons J T, Stulen F L. US Patent 2820187, 1958-01-14
4	Koren Y. The rapid responsiveness of RMS. International Journal of Production Research, 2013, 51(23–24): 6817–6827 https://doi.org/10.1080/00207543.2013.856528
5	Koren Y, Wang W, Gu X. Value creation through design for scalability of reconfigurable manufacturing systems. International Journal of Production Research, 2017, 55(5): 1227–1242 https://doi.org/10.1080/00207543.2016.1145821
6	Koren Y. The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems. Hoboken: John Wiley & Sons, 2010
7	Garbie I H. DFSME: Design for sustainable manufacturing enterprises (an economic viewpoint). International Journal of Production Research, 2013, 51(2): 479–503 https://doi.org/10.1080/00207543.2011.652746
8	Garbie I H. An analytical technique to model and assess sustainable development index in manufacturing enterprises. International Journal of Production Research, 2014, 52(16): 4876–4915 https://doi.org/10.1080/00207543.2014.893066
9	Koren Y, Shpitalni M. Design of reconfigurable manufacturing systems. Journal of Manufacturing Systems, 2010, 29(4): 130–141 https://doi.org/10.1016/j.jmsy.2011.01.001
10	Zhang G, Liu R, Gong L, et al.An analytical comparison on cost and performance among DMS, AMS, FMS and RMS. In: Dashchenko A I, ed. Reconfigurable Manufacturing Systems and Transformable Factories. Berlin: Springer, 2006, 659–673
11	Singh A, Gupta S, Asjad M, et al.Reconfigurable manufacturing systems: Journey and the road ahead. International Journal of System Assurance Engineering and Management, 2017, 1–9 (in press)
12	Wang W, Koren Y. Scalability planning for reconfigurable manufacturing systems. Journal of Manufacturing Systems, 2012, 31(2): 83–91 https://doi.org/10.1016/j.jmsy.2011.11.001
13	Maier-Speredelozzi V, Koren Y, Hu S J. Convertibility measures for manufacturing systems. CIRP Annals-Manufacturing Technology, 2003, 52(1): 367–370 https://doi.org/10.1016/S0007-8506(07)60603-9
14	Gumasta K, Gupta S K, Benyouce L, et al.Developing a reconfigurability index using multi-attribute utility theory. International Journal of Production Research, 2011, 49(6): 1669–1683 https://doi.org/10.1080/00207540903555536
15	Bi Z M, Lang S Y T, Shen W, et al.Reconfigurable manufacturing systems: The state of the art. International Journal of Production Research, 2008, 46(4): 967–992 https://doi.org/10.1080/00207540600905646
16	Bi Z M, Wang L, Lang S T Y. Current status of reconfigurable assembly systems. International Journal of Manufacturing Research, 2007, 2(3): 303–328 https://doi.org/10.1504/IJMR.2007.014727
17	Colledani M, Tolio T. A decomposition method to support the reconfiguration/reconfiguration of production systems. CIRP Annals-Manufacturing Technology, 2005, 54 (1): 441–444 https://doi.org/10.1016/S0007-8506(07)60140-1
18	Li J, Dai X, Meng Z. Automatic reconfiguration of petri net controllers for reconfigurable manufacturing systems with an improved net rewriting system-based approach. IEEE Transactions on Automation Science and Engineering, 2009, 6(1): 156–167 https://doi.org/10.1109/TASE.2008.2006857
19	Meng X. Modeling of reconfigurable manufacturing systems based on colored timed object-oriented Petri nets. Journal of Manufacturing Systems, 2010, 29(2–3): 81–90 https://doi.org/10.1016/j.jmsy.2010.11.002
20	Zhao X, Wang K, Luo Z. A stochastic model of a reconfigurable manufacturing system Part I: A framework. International Journal of Production Research, 2000, 38(10): 2273–2285 https://doi.org/10.1080/00207540050028098
21	Rösiö C, Säfsten K. Reconfigurable production system design––Theoretical and practical challenges. Journal of Manufacturing Technology Management, 2013, 24(7): 998–1018 https://doi.org/10.1108/JMTM-02-2012-0021
22	Andersen A L, Brunoe T D, Nielsen K, et al.Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools. Journal of Manufacturing Systems, 2017, 42(1): 179–195 https://doi.org/10.1016/j.jmsy.2016.11.006
23	Koren Y, Kota S. US Patent 5943750, 1999-08-31
24	Koren Y, Katz R. US Patent 6567162, 2003-12-24
25	Koren Y, Ulsoy G. US Patent 6349237, 2002-02-19
26	Krygier R. The Integration of flexible, reconfigurable manufacturing with quality. In: Proceedings of CIRP 3rd Conference on Reconfigurable Manufacturing. Ann Arbor, 2005
27	Gadalla M, Xue D. Recent advances in research on reconfigurable machine tools: A literature review. International Journal of Production Research, 2017, 55(5): 1440–1454 https://doi.org/10.1080/00207543.2016.1237795
28	Koren Y, Hu S J, Weber T W. Impact of manufacturing system configuration on performance. CIRP Annals-Manufacturing Technology, 1998, 47(1): 369–372 https://doi.org/10.1016/S0007-8506(07)62853-4
29	Freiheit T, Shpitalni M, Hu S J, et al.Designing productive manufacturing systems without buffers. CIRP Annals-Manufacturing Technology, 2003, 52 (1): 105–108. https://doi.org/10.1016/S0007-8506(07)60542-3
30	Gu X. The impact of maintainability on the manufacturing system architecture. International Journal of Production Research, 2017, 55(15): 4392–4410 https://doi.org/10.1080/00207543.2016.1254356
31	Koren Y, Gu X, Guo W. Choosing the system configuration for high-volume manufacturing. International Journal of Production Research, 2017 (in press) https://doi.org/10.1080/00207543.2017.1387678
32	Youssef A M A, ElMaraghy H A. Availability consideration in the optimal selection of multiple-aspect RMS configurations. International Journal of Production Research, 2008, 46(21): 5849–5882 https://doi.org/10.1080/00207540701261626
33	Dou J, Dai X, Meng Z. Optimization for multipart flow-line configuration of reconfigurable manufacturing system using GA. International Journal of Production Research, 2010, 48(14): 4071–4100 https://doi.org/10.1080/00207540903036305
34	Goyal K K, Jain P K, Jain M. Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. International Journal of Production Research, 2012, 50(15): 4175–4191 https://doi.org/10.1080/00207543.2011.599345
35	Webbink R F, Hu S J. Automated generation of assembly system-design solutions. IEEE Transactions on Automation Science and Engineering, 2005, 2(1): 32–39 https://doi.org/10.1109/TASE.2004.840072
36	Benkamoun N, Huyet A L, Kouiss K. Reconfigurable assembly system configuration design approaches for product change. In: Proceedings of the 2013 IEEE International Conference on Industrial Engineering and Systems Management (IESM). Rabat: IEEE, 2013
37	Narongwanich W, Duenyas I, Birge J R. Optimal Portfolio of Reconfigurable and Dedicated Capacity Under Uncertainty. Technical Report, University of Michigan ERC-RMS, 2002
38	Deif A M, ElMaraghy W. Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems. International Journal of Flexible Manufacturing Systems, 2006, 18(3): 225–238 https://doi.org/10.1007/s10696-006-9006-0
39	Gyulai D, Kádár B, Kovács A, et al.Capacity management for assembly systems with dedicated and reconfigurable resources. CIRP Annals-Manufacturing Technology, 2014, 63(1): 457–460 https://doi.org/10.1016/j.cirp.2014.03.110
40	Renna P. A decision investment model to design manufacturing systems based on a genetic algorithm and Monte-Carlo simulation. International Journal of Computer Integrated Manufacturing, 2017, 30(6): 590–605 https://doi.org/10.1080/0951192X.2016.1187299
41	Asl F M, Ulsoy A G. Stochastic optimal capacity management in reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology 2003, 52 (1): 371–374
42	Spicer P, Carlo H J. Integrating reconfiguration cost into the design of multi-period scalable reconfigurable manufacturing systems. Journal of Manufacturing Science and Engineering, 2007, 129(1): 202–210 https://doi.org/10.1115/1.2383196
43	Carlo H J, Spicer J P, Rivera-Silva A. Simultaneous consideration of scalable-reconfigurable manufacturing system investment and operating costs. Journal of Manufacturing Science and Engineering, 2012, 134(1): 011003 https://doi.org/10.1115/1.4005305
44	Van Mieghem J A. Investment strategies for flexible resources. Management Science, 1998, 44(8): 1071–1078 https://doi.org/10.1287/mnsc.44.8.1071
45	Ceryan O, Koren Y. Manufacturing capacity planning strategies. CIRP Annals-Manufacturing Technology, 2009, 58(1): 403–406
46	Matta A, Tomasella M, Clerici M, et al.Optimal reconfiguration policy to react to product changes. International Journal of Production Research, 2008, 46(10): 2651–2673 https://doi.org/10.1080/00207540701452159
47	Bryan A, Ko J, Hu S J, et al.Co-evolution of product families and assembly systems. CIRP Annals-Manufacturing Technology, 2007, 56(1): 41–44 https://doi.org/10.1016/j.cirp.2007.05.012
48	Matta A, Tomasella M, Valente A. Impact of ramp-up on the optimal capacity-related reconfiguration policy. International Journal of Flexible Manufacturing Systems, 2007, 19(3): 173–194 https://doi.org/10.1007/s10696-007-9023-7
49	Niroomand I, Kuzgunkaya O, Bulgak A A. Impact of reconfiguration characteristics for capacity investment strategies in manufacturing systems. International Journal of Production Economics, 2012, 139(1): 288–301 https://doi.org/10.1016/j.ijpe.2012.05.012
50	Shi J. Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. Boca Raton: CRC Press, 2006
51	Hu S J, Koren Y. Stream-of-variation theory for automotive body assembly. CIRP Annals-Manufacturing Technology, 1997, 46(1): 1–6 https://doi.org/10.1016/S0007-8506(07)60763-X
52	Hu S J, Stecke K E. Analysis of automotive body assembly system configurations for quality and productivity. International Journal of Manufacturing Research, 2009, 4(3): 281–305 https://doi.org/10.1504/IJMR.2009.026575
53	Kristianto Y, Gunasekaran A, Jiao J. Logical reconfiguration of reconfigurable manufacturing systems with stream of variations modelling: A stochastic two-stage programming and shortest path model. International Journal of Production Research, 2014, 52(5): 1401–1418 https://doi.org/10.1080/00207543.2013.843798
54	Abad A, Guo W, Jin J. Algebraic expression of system configurations and performance metrics for mixed model assembly systems. IIE Transactions, 2014, 46(3): 230–248 https://doi.org/10.1080/0740817X.2013.813093
55	Gupta A, Jain P K, Kumar D. A novel approach for part family formation using K-means algorithm. Advances in Manufacturing, 2013 1(3): 241–250
56	Kimura F, Nielsen J. A design for product family under manufacturing resource constraints. CIRP Annals-Manufacturing Technology, 2005, 54 (1): 139–142 https://doi.org/10.1016/S0007-8506(07)60068-7
57	Abdi M R, Labib A W. Grouping and selecting products: The design key of reconfigurable manufacturing systems (RMSs). International Journal of Production Research, 2004, 42(3): 521–546 https://doi.org/10.1080/00207540310001613665
58	Abdi M R, Labib A W. A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): A case study. International Journal of Production Research, 2003, 41(10): 2273–2299 https://doi.org/10.1080/0020754031000077266
59	Galan R, Racero J, Eguia I, et al.A systematic approach for product families formation in reconfigurable manufacturing systems. Robotics and Computer-integrated Manufacturing, 2007, 23(5): 489–502 https://doi.org/10.1016/j.rcim.2006.06.001
60	Abdi M R. Product family formation and selection for reconfigurability using analytical network process. International Journal of Production Research, 2012, 50(17): 4908–4921 https://doi.org/10.1080/00207543.2012.657976
61	Battaïa O, Dolgui A, Guschinsky N. Decision support for design of reconfigurable rotary machining systems for family part production. International Journal of Production Research, 2017, 55(5): 1368–1385 https://doi.org/10.1080/00207543.2016.1213451
62	Goyal K K, Jain P K, Jain M. A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable manufacturing system. International Journal of Production Research, 2013, 51(6): 1762–1776 https://doi.org/10.1080/00207543.2012.701771
63	Wang G, Huang S, Shang X, et al.Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines. Journal of Manufacturing Systems, 2016, 41: 120–129 https://doi.org/10.1016/j.jmsy.2016.08.009
64	Kashkoush M, ElMaraghy H. Product family formation for reconfigurable assembly systems. Procedia CIRP, 2014, 17: 302–307 https://doi.org/10.1016/j.procir.2014.01.131
65	Eguia I, Lozano S, Racero J, et al.A methodological approach for designing and sequencing product families in reconfigurable disassembly systems. Journal of Industrial Engineering and Management, 2011, 4(3): 418–435 https://doi.org/10.3926/jiem.2011.v4n3.p418-435
66	Azab A, ElMaraghy H. Mathematical modeling for reconfigurable process planning. CIRP Annals-Manufacturing Technology, 2007, 56(1): 467–472
67	Azab A, Perusi G, ElMaraghy H A, et al.Semi-generative macro-process planning for reconfigurable manufacturing. Digital Enterprise Technology, 2007, 251–258
68	Bensmaine A, Dahane M, Benyoucef L. A simulation-based genetic algorithm approach for process plans selection in uncertain reconfigurable environment. IFAC Proceedings Volumes, 2013, 46(9): 1961–1966
69	Bensmaine A, Dahane M, Benyoucef L. A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. International Journal of Production Research, 2014, 52(12): 3583–3594 https://doi.org/10.1080/00207543.2013.878056
70	Borisovsky P A, Delorme X, Dolgui A. Genetic algorithm for balancing reconfigurable machining lines. Computers & Industrial Engineering, 2013, 66(3): 541–547 https://doi.org/10.1016/j.cie.2012.12.009
71	Borisovsky P A, Delorme X, Dolgui A. Balancing reconfigurable machining lines via a set partitioning model. International Journal of Production Research, 2014, 52(13): 4026–4036 https://doi.org/10.1080/00207543.2013.849857
72	Essafi M, Delorme X, Dolgui A. A reactive GRASP and path relinking for balancing reconfigurable transfer lines. International Journal of Production Research, 2012, 50(18): 5213–5238 https://doi.org/10.1080/00207543.2012.677864
73	Makssoud F, Battaïa O, Dolgui A. Reconfiguration of machining transfer lines. In: Borangiu T, Thomas A, Trentesaux D, eds. Service Orientation in Holonic and Multi Agent Manufacturing and Robotics. Berlin: Spring, 2013, 339–353
74	Delorme X, Malyutin S, Dolgui A. A multi-objective approach for design of reconfigurable transfer lines. IFAC-PapersOnLine, 2016, 49(12): 509–514 https://doi.org/10.1016/j.ifacol.2016.07.675
75	da Silva R M, Junqueira F, Santos Filho D J, et al.Control architecture and design method of reconfigurable manufacturing systems. Control Engineering Practice, 2016, 49: 87–100 https://doi.org/10.1016/j.conengprac.2016.01.009
76	Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 2000, 11(4): 403–419 https://doi.org/10.1023/A:1008930403506
77	Ni J, Jin X. Decision support systems for effective maintenance operations. CIRP Annals-Manufacturing Technology, 2015, 61(1): 411–414 https://doi.org/10.1016/j.cirp.2012.03.065
78	Guo W, Jin J, Hu S J. Allocation of maintenance resources in mixed model assembly systems. Journal of Manufacturing Systems, 2013, 32(3): 473–479 https://doi.org/10.1016/j.jmsy.2012.12.006
79	Gu X, Jin X, Ni J. Prediction of passive maintenance opportunity windows on bottleneck machines in complex manufacturing systems. ASME Journal Manufacturing Science and Engineering, 2015, 137(3): 031017 https://doi.org/10.1115/1.4029906
80	Ni J, Gu X, Jin X. Preventive maintenance opportunities for large production systems. CIRP Annals-Manufacturing Technology, 2015, 64(1): 447–450 https://doi.org/10.1016/j.cirp.2015.04.127
81	Gu X, Jin X, Guo W, et al.Estimation of active maintenance opportunity windows in Bernoulli production lines. Journal of Manufacturing Systems, 2017, 45: 109–120
82	Zhou J, Djurdjanovic D, Ivy D, et al.Integrated reconfiguration and age-based preventive maintenance decision making. IIE Transactions, 2007, 39(12): 1085–1102 https://doi.org/10.1080/07408170701291779
83	Xia T, Xi L, Pan E, et al.Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems. Reliability Engineering & System Safety, 2017, 166: 87–98 https://doi.org/10.1016/j.ress.2016.09.001
84	Xia T, Tao X, Xi L. Operation process rebuilding (OPR)-oriented maintenance policy for changeable system structures. IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 139–148 https://doi.org/10.1109/TASE.2016.2618767
85	Brettel M, Klein M, Friederichsen N. The relevance of manufacturing flexibility in the context of Industrie 4.0. Procedia CIRP, 2016, 41: 105–110 https://doi.org/10.1016/j.procir.2015.12.047
86	Dubey R, Gunasekaran A, Helo P, et al.Explaining the impact of reconfigurable manufacturing systems on environmental performance: The role of top management and organizational culture. Journal of Cleaner Production, 2017, 141: 56–66 https://doi.org/10.1016/j.jclepro.2016.09.035
87	Michalek J J, Ceryan O, Papalambros P Y, et al.Balancing marketing and manufacturing objectives in product line design. Journal of Mechanical Design, 2006, 128(6): 1196–1204 https://doi.org/10.1115/1.2336252
88	Tang L, Yip-Hoi D M, Wang W, et al.Concurrent line-balancing, equipment selection and throughput analysis for multi-part optimal line design. Journal for Manufacturing Science and Production, 2004, 6(1–2): 71–82 https://doi.org/10.1515/IJMSP.2004.6.1-2.71
89	Ausaf M F, Gao L, Li X. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm. Frontiers of Mechanical Engineering, 2015, 10(4): 392–404 https://doi.org/10.1007/s11465-015-0353-y
90	Wang B, Guan Z, Chen Y, et al.An assemble-to-order production planning with the integration of order scheduling and mixed-model sequencing. Frontiers of Mechanical Engineering, 2013, 8(2): 137–145 https://doi.org/10.1007/s11465-013-0251-0
91	Renzi C, Leali F, Cavazzuti M, et al.A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. International Journal of Advanced Manufacturing Technology, 2014, 72(1–4): 403–418 https://doi.org/10.1007/s00170-014-5674-1
92	Koren Y, Gu X, Freiheit T. The impact of corporate culture on manufacturing system design. CIRP Annals-Manufacturing Technology, 2016, 65(1): 413–416
93	He N, Zhang D Z, Li Q. Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system. International Journal of Production Economics, 2014, 149: 117–130 https://doi.org/10.1016/j.ijpe.2013.08.022
94	Gao R, Wang L, Teti R, et al.Cloud-enabled prognosis for manufacturing. CIRP Annals-Manufacturing Technology, 2015, 64(2): 749–772
95	Xiong Y, Yin Z. Digital manufacturing––The development direction of the manufacturing technology in the 21st century. Frontiers of Mechanical Engineering in China, 2006, 1(2): 125–130 https://doi.org/10.1007/s11465-006-0021-3
96	Monostori L, Kádár B, Bauernhansl T, et al.Cyber-physical systems in manufacturing. CIRP Annals-Manufacturing Technolo-gy, 2016, 65(2): 621–641 https://doi.org/10.1016/j.cirp.2016.06.005
97	Guo W, Chen R, Jin J. On-line eccentricity monitoring of seamless tubes in cross-roll piercing mill. ASME Journal Manufacturing Science and Engineering, 2015, 137(2): 021007 https://doi.org/10.1115/1.4028440
98	Guo W, Shao C, Kim T H, et al.Online process monitoring with near-zero misdetection for ultrasonic welding of Lithium-ion batteries. Journal of Manufacturing Systems, 2016, 38(1): 141–150 https://doi.org/10.1016/j.jmsy.2016.01.001
99	Wang S, Chen T, Sun J. Design and realization of a remote monitoring and diagnosis and prediction system for large rotating machinery. Frontiers of Mechanical Engineering in China, 2010, 5(2): 165–170 https://doi.org/10.1007/s11465-009-0090-1
100	Li X, Jiang J, Su H, et al.Identification of abnormal operating conditions and intelligent decision system. Frontiers of Mechanical Engineering in China, 2011, 6(4): 456–462 https://doi.org/10.1007/s11465-011-0224-0
101	Xu X, Deng S. Trend prediction technology of condition maintenance for large water injection units. Frontiers of Mechanical Engineering, 2010, 5(2): 171–175 https://doi.org/10.1007/s11465-009-0091-0
102	Hu Y, Yang S, Du R. Distributed flexible reconfigurable condition monitoring and diagnosis technology. Frontiers of Mechanical Engineering in China, 2006, 1(3): 276–281 https://doi.org/10.1007/s11465-006-0025-z
103	Lee J, Lapira E, Bagheri B, et al.Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 2013, 1(1): 38–41 https://doi.org/10.1016/j.mfglet.2013.09.005
104	Guo W, Guo S, Wang H, et al.A data-driven diagnostic system utilizing manufacturing data mining and analytics. SAE International Journal of Materials and Manufacturing, 2017, 10(3): 01632923 https://doi.org/10.4271/2017-01-0233
105	Guo N, Leu M C. Additive manufacturing: Technology, applications and research needs. Frontiers of Mechanical Engineering, 2013, 8(3): 215–243 https://doi.org/10.1007/s11465-013-0248-8
106	Koren Y, Hu S J, Gu P, et al.Open architecture products. CIRP Annals-Manufacturing Technology, 2013, 62(2): 719–729
107	Hu S J, Ko J, Weyand L, et al.Assembly system design and operations for product variety. CIRP Annals-Manufacturing Technology, 2011, 60(2): 715–733 https://doi.org/10.1016/j.cirp.2011.05.004
108	Koren Y, Hill R. US Patent 6920973, 2004-07-26
109	Cherubini A, Passama R, Crosnier A, et al.Collaborative manufacturing with physical human-robot interaction. Robotics and Computer-Integrated Manufacturing, 2016, 40: 1–13 https://doi.org/10.1016/j.rcim.2015.12.007
110	Pellegrinelli S, Moro F L, Pedrocchi N, et al.A probabilistic approach to workspace sharing for human-robot cooperation in assembly tasks. CIRP Annals-Manufacturing Technology, 2016, 65(1): 57–60 https://doi.org/10.1016/j.cirp.2016.04.035
111	Wang X V, Kemény Z, Váncza J, et al.Human-robot collaborative assembly in cyber-physical production: Classification framework and implementation. CIRP Annals-Manufacturing Technology, 2017, 66(1): 5–8 https://doi.org/10.1016/j.cirp.2017.04.101

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