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Machine Tool 4.0 for the new era of manufacturing

ORIGINAL ARTICLE

Abstract

The widespread use and continuous improvements of machine tools have had a significant impact on productivity in manufacturing industry ever since the Industrial Revolution. At the dawn of the new era of industrialization, the need to advance machine tools to a new level that accords to the concept of Industrie 4.0 has to be recognised and addressed. Muck like the different stages of industrialisation, machine tools have also gone through different stages of technological advancements, i.e., Machine Tool 1.0, Machine Tool 2.0 and Machine Tool 3.0. Industrie 4.0 pleads for a new generation of machines—Machine Tool 4.0. This paper describes some of the key and desired characteristics of Machine Tool 4.0 such as Cyber-physical Machine Tools, vertically and horizontally integrated machine tools and more intelligent, autonomous and safer machine tools.

Keywords

Machine tools Machine Tool 4.0 CNC Industrie 4.0 Cyber-physical systems (CPS) Cyber-physical machine tools (CPMT) 

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References

  1. 1.
    Lee EA (2010) CPS Foundations, In: Proceedings of Design Automation Conference (DAC), ACMGoogle Scholar
  2. 2.
    Derler P, Lee EA, Sangiovanni-Vincentelli A (2012) Modeling cyber-physical systems, In: Proceedings of the IEEE (special issue on CPS), 100(1):13–28Google Scholar
  3. 3.
    acatech. (2015) Securing the future of German manufacturing industry: recommendations for implementing the strategic initiative INDUSTRIE 4.0 (Final report of the Industrie 4.0 Working Group). acatech – National Academy of Science and Engineering, GermanyGoogle Scholar
  4. 4.
    European Commission (2013) Factories of the future: multi-annual roadmap for the contractual PPP under HORIZON 2020. Publications Office of the European Union. ISBN 978-92-79-31238-0. European UnionGoogle Scholar
  5. 5.
    Moore WR (1970) Foundations of mechanical accuracy (1st ed.), Bridgeport, Connecticut, USA: Moore Special Tool CoGoogle Scholar
  6. 6.
    Cheng T, Zhang J, Hu C, Wu B, Yang S (2001) Intelligent machine tools in a distributed network manufacturing mode environment. Int J Adv Manuf Technol 17(3):221–232CrossRefGoogle Scholar
  7. 7.
    Allcock A (2006) Much more than DNC. Machinery 164(4120):16–20Google Scholar
  8. 8.
    Xu X. (2009) Integrating advanced computer-aided design, manufacturing, and numerical control: principles and implementations. IGI Global. ISBN: 978-1-59904-714-0Google Scholar
  9. 9.
    EIA (1979) Standard RS-274-D: Interchangeable Variable Block Data Format for Positioning, Contouring, and Contouring/Positioning Numerically Controlled Machines, Washington, D.C. USA. Electron Ind AssocGoogle Scholar
  10. 10.
    ISO 6983-1 (1982) Numerical control of machines—program format and definition of address words—part 1: data format for positioning, line motion and contouring control systems. Chemin de Blandonnet 8. CP 401 1214 Vernier, Geneva. SwitzerlandGoogle Scholar
  11. 11.
    Behrendt T, Zein A, Min S (2012) Development of an energy consumption monitoring procedure for machine tools. CIRP Ann Manuf Technol 61(1):43–46CrossRefGoogle Scholar
  12. 12.
    Mori M, Fujishima M, Inamasu Y, Oda Y (2011) A study on energy efficiency improvement for machine tools. CIRP Ann Manuf Technol 60(1):145–148CrossRefGoogle Scholar
  13. 13.
    Moriwaki T (2008) Multi-functional machine tool. CIRP Ann Manuf Technol 57(2):736–749MathSciNetCrossRefGoogle Scholar
  14. 14.
    Uhlmann E, Eßmann J, Wintering J-H (2012) Design- and control-concept for compliant machine tools based on controller integrated models. CIRP Ann Manuf Technol 61(1):347–350CrossRefGoogle Scholar
  15. 15.
    Abele E, Altintas Y, Brecher C (2010) Machine tool spindle units. CIRP Ann Manuf Technol 59(2):781–802CrossRefGoogle Scholar
  16. 16.
    Neugebauer R, Denkena B, Wegener K (2007) Mechatronic systems for machine tools. CIRP Ann Manuf Technol 56(2):657–686CrossRefGoogle Scholar
  17. 17.
    Xu Y, Zhang L, Wang S, Du H, Chai B, Hu SJ (2015) Active precision design for complex machine tools: methodology and case study. Int J Adv Manuf Technol 80(1–4):581–590CrossRefGoogle Scholar
  18. 18.
    Nassehi A, Newman ST (2012) Modeling of machine tools using smart interlocking software blocks. CIRP Ann Manuf Technol 61(1):435–438CrossRefGoogle Scholar
  19. 19.
    Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann Manuf Technol 58(2):588–607CrossRefGoogle Scholar
  20. 20.
    Kjellberg T, von Euler-Chelpin A, Hedlind M, Lundgren M, Sivard G, Chen D (2009) The machine tool model-A core part of the digital factory. CIRP Ann Manuf Technol 58(1):425–428CrossRefGoogle Scholar
  21. 21.
    Yang W, Xu X (2008) Modelling machine tool data in support of STEP-NC based manufacturing. Int J Comput Integr Manuf 21(7):745–763CrossRefGoogle Scholar
  22. 22.
    ISO 14649-1 (2003) Industrial automation systems and integration—physical device control—data model for computerized numerical controllers—part 1: overview and fundamental principles. Chemin de Blandonnet 8. CP 401. 1214 Vernier, Geneva. SwitzerlandGoogle Scholar
  23. 23.
    ISO 10303-238 (2007) Industrial automation systems and integration -- Product data representation and exchange -- Part 238: application protocol: application interpreted model for computerized numerical controllers. Chemin de Blandonnet 8. CP 401. 1214 Vernier, Geneva. SwitzerlandGoogle Scholar
  24. 24.
    Suh SH, Cheon SU (2002) A framework for an intelligent CNC and data model. Int J Adv Manuf Technol 19:727–735CrossRefGoogle Scholar
  25. 25.
    Suh SH, Cho JH, Hong HD (2002) On the architecture of intelligent STEP-compliant CNC. Int J Comput Integr Manuf 15:168–177CrossRefGoogle Scholar
  26. 26.
    Xu X, Wang H, Mao J, Newman ST, Kramer TR, Proctor FM, Michaloski JL (2005) STEP-compliant NC research: the search for intelligent CAD/CAPP/CAM/CNC integration. Int J Prod Res 43(17):3703–3743CrossRefGoogle Scholar
  27. 27.
    Xu X (2006) Realisation of STEP-NC enabled machining. Robot Comput Integr Manuf 22(2):144–153CrossRefGoogle Scholar
  28. 28.
    Venkatesh S, Odendahl D, Xu X, Michaloski J, Proctor F, Kramer T (2007) Boeing, NIST help to take STEP-NC to new heights”. Tooling and Production, pp 28–31Google Scholar
  29. 29.
    Xu X, Nee AYC (2010) Advanced Design and Manufacturing Based on STEP, (Edited). Springer Veralag. January, 2010. ISBN: 978-1-84882-738-7Google Scholar
  30. 30.
    Ridwan F, Xu X, Liu G (2012) A framework for machining optimisation based on STEP-NC. J Intell Manuf 23(3):423–441CrossRefGoogle Scholar
  31. 31.
    Hardwick M, Zhao YF, Proctor FM, Nassehi A, Xu X et al (2013) A roadmap for STEP-NC enabled interoperable manufacturing. Int J Adv Manuf Technol 68:1023–1037CrossRefGoogle Scholar
  32. 32.
    ISO 10303-1 (1994) Industrial automation systems and integration—product data representation and exchange—part 1: overview and fundamental principles. Chemin de Blandonnet 8. CP 401. 1214 Vernier, Geneva. SwitzerlandGoogle Scholar
  33. 33.
    Okuma OSP control. http://www.okuma.com/osp-p-control. (Accessed on 20 November 2015)
  34. 34.
    KUKA.PLC mxA. One interface for all. PF0003/E/2/0313. KUKA Roboter GmbH Hery-Park 3000, 86368 Gersthofen, GermanyGoogle Scholar
  35. 35.
    MTConnect. Association for manufacturing technology and MTConnect Institute. 7901 Westpark Drive. McLean, VA 22102. USAGoogle Scholar
  36. 36.
    OPC UA. (2006) OPC Foundation: OPC UA Specification: part 1—concepts. Version 1.00Google Scholar
  37. 37.
    Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters 3:18–23CrossRefGoogle Scholar
  38. 38.
    Essex D (2014) Industrial Internet Consortium tackles interoperability. TechTargetGoogle Scholar
  39. 39.
    Industrial Internet Consortium, Needham, Massachusetts, USA. http://www.iiconsortium.org
  40. 40.
    Lee J, Kao HA, Yang S (2014) Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 16:3–8CrossRefGoogle Scholar
  41. 41.
    Acatech (2015) SMART SERVICE WELT: recommendations for the strategic initiative web-based services for businesses. Final Report. Long Version. acatech – National Academy of Science and Engineering, GermanyGoogle Scholar
  42. 42.
    Greenough RM, Grubic T (2011) Modelling condition-based maintenance to deliver a service to machine tool users. Int J Adv Manuf Technol 52(9–12):1117–1132CrossRefGoogle Scholar
  43. 43.
    Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86CrossRefGoogle Scholar
  44. 44.
    Wang XV, Xu X (2013) An interoperable solution for cloud manufacturing. Robot Comput Integr Manuf 29:232–247MathSciNetCrossRefGoogle Scholar
  45. 45.
    Wang XV, Xu X (2014) Virtualize manufacturing capabilities in the Cloud: requirements, architecture and implementation. Int J Manuf Res 9(4):348–368MathSciNetCrossRefGoogle Scholar
  46. 46.
    Lu Y, Xu X, Xu J (2014) Development of a hybrid manufacturing cloud. J Manuf Syst 33:551–566CrossRefGoogle Scholar
  47. 47.
    Ko, RKL, Lee BSG, Pearson S (2011) Towards achieving accountability, auditability and trust in cloud computing, in International workshop on cloud computing: architecture, algorithms and applications (CloudComp2011), Kochi, India, pp. 5–18Google Scholar
  48. 48.
    Ko RKL, Lee BSG, Rajan V (2013) Understanding cloud failures. IEEE Spectr 49(12):84Google Scholar
  49. 49.
    Schnorr C-P (1991) Efficient signature generation by smart cards. J Cryptol 4:161–174CrossRefzbMATHGoogle Scholar
  50. 50.
    Shen J-J, Lin C-W, Hwang M-S (2003) A modified remote user authentication scheme using smart cards. IEEE Trans Consum Electron 49:414–416CrossRefGoogle Scholar
  51. 51.
    Daemen J, Rijmen V (2002) The design of Rijndael: AES-the advanced encryption standard: SpringerGoogle Scholar
  52. 52.
    Bernstein DJ (2005) Salsa20 specification, eSTREAM Project algorithm description, http://www.ecrypt.eu.org/stream/salsa20pf.html.
  53. 53.
    Bernstein DJ (2008) ChaCha, a variant of Salsa20, in Workshop Record of SASCGoogle Scholar
  54. 54.
    Clarke G (2013) Microsoft’s windows azure plan B: a hard drive, a courier and a data-centre monkey. http://www.theregister.co.uk/2013/11/05/windows_azure_hard_drive_import_exp ort/
  55. 55.
    Fielding RT (2000) Architectural styles and the design of network-based software architectures. University of California, USAGoogle Scholar
  56. 56.
    Fielding RT, Taylor RN (2002) Principled design of the modern web architecture. ACM Trans Internet Technol (TOIT) 2:115–150CrossRefGoogle Scholar
  57. 57.
    Ko RKL, Kirchberg M, Lee BS, Chew E (2012) Overcoming large data transfer bottlenecks in RESTful service orchestrations, in Web Services (ICWS). IEEE 19th Int Conf 654–656Google Scholar
  58. 58.
    Gu Y, Grossman RL (2003) UDT: an application level transport protocol for grid computing, in second International workshop on protocols for fast long- distance networksGoogle Scholar
  59. 59.
    Gu Y, Grossman RL (2007) UDT: UDP-based data transfer for high-speed wide area networks. Comput Netw 51:1777–1799CrossRefzbMATHGoogle Scholar
  60. 60.
    Rumble SM, Ongaro D,Stutsman R, Rosenblum M, Ousterhout JK (2011) It’s time for low latency, in Proceedings of the 13th USENIX conference on hot topics in operating systems, pp. 11–11Google Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of AucklandAucklandNew Zealand

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