Multi-objective Optimization for Error Compensation in Intelligent Micro-factory CPS

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 540)

Abstract

In the last decade, the demand of micro products and miniaturization has seen a wide spread growth. Currently, micro products and micro features are produced through conventional macro scale ultra-precision machines and MEMS manufacturing techniques. These technologies have limitations as conventional machining centers consume large energy and space. For mass production of micro components using non-silicon materials and real 3D shapes or free-form surfaces, mechanical micro manufacturing technology based machine tools are developed as an alternative method. The principle of “Small equipment for small parts” is gaining trend towards the investigation on micro-machine tools. One example of miniaturization of manufacturing equipment and systems is the Japanese micro-factory concept. Few micro-machines and associated handling micro grippers and transfer arms are developed to create micro-factory. The manufacturing processes are performed in a desktop factory environment. To explore the micro-factory idea, large number of micro machines can be installed in a small work-floor. The control of this micro factory concept for operation, maintenance and monitoring becomes a Cyber-physical system capable of producing micro-precision products in a fully-automated manner at low cost. Manufacturing processing data and condition monitoring of micro machine tools in a micro factory are the variables of interest to run a smooth process flow. Every machine out of hundreds of micro machines will have sensing equipment and the sensors data is being compiled at one place, ideally using wireless communication systems. One or two operators can run and monitor the whole micro-factory and access the machine if the fault alarms receive from any station. A variety of sensors will be employed for machine control, process control, metrology and calibration, condition monitoring of machine tools, assembly and integration technology at the micro-scale resulting in smooth operation of micro-factory. Single machine can be designed with a computer numerical control, but, flexible reconfigurable controllers are envisioned to control variety of processes that will lead to the development of open architecture controllers to operate micro-factory. Therefore, the control effort and algorithms have to utilize process models to improve the overall process and, ultimately, the product. Thus, we aim to introduce machine to machine (M2M) communication in the micro factory test bed. M2M communication enables micro actuator/sensor & controller devices to communicate with each other directly i.e., without human intervention, automating management, monitoring, and data collection between devices, as well as communicating with neighboring machines. All micro sensors communicate with a local short distance wireless network e.g. via Bluetooth piconet as well as with a centralized controller via WLAN 802.11 to exchange control/command from it. In this chapter, inherent issues are first highlighted where bulk micro-part manufacturing is carried out using large size machines. State-of-the-art micro machine tool systems designed and developed so far are discussed. With the help of precision engineering fundamentals and miniaturization scaling issues, a design strategy is formulated for a high precision 3-axis CNC micro machine tool as a model for micro-factory working. Based on this, a mathematical model is built that includes machine’s design variables and its inherent errors. The volumetric error between tool/work-piece is evaluated from the machine’s mathematical model and further used as an objective function to be minimized. Robust design optimization at micro machine development stage reveals the sensitivity analysis of each design variable. The optimization analysis employs different design of Experiment (DOE) techniques to make initial population that is governed by multi-objective genetic algorithm. Hence, the robust design is achieved for 3-axis micro machine tool using the essential knowledge base. The technique is used to remove the machine’s repeatable scale errors via calibration and is known as error mapping. These errors are entered into the machine controller, which has the capability of compensating for the error. The machine does not need any extra hardware. Error mapping is a cost-effective tool in achieving volumetric accuracy in a micro manufacturing system.

Keywords

Micro factory Micro machines Robotic cyber physical system Machine to machine communication Volumetric error Error compensation 

References

  1. 1.
    M.A. Rahman, M. Rahman, A.S. Kumar, H.S. Lim, CNC microturning: an application to miniaturization. Int. J. Mach. Tools Manuf. 45(6), 631–639 (2005)CrossRefGoogle Scholar
  2. 2.
    Y. Takeuchi, H. Yonekura, K. Sawada, Creation of 3-D tiny statue by 5-axis control ultra-precision machining. Comput. Aided Des. 35(4), 403–409 (2003)CrossRefGoogle Scholar
  3. 3.
    http://www.agie.com. Accessed July 2013
  4. 4.
    http://www.primacon.de. Accessed July 2013
  5. 5.
    http://www.fanuc.co.jp. Accessed July 2013
  6. 6.
    http://www.nanotechsys.com. Accessed July 2013
  7. 7.
    Y. Okazaki, N. Mishima, K. Ashida, Microfactory-concept, history and developments. J. Manuf. Sci. Eng. 126(4), 837–844 (2004)CrossRefGoogle Scholar
  8. 8.
    Z. Lu, T. Yoneyama, Micro cutting in the micro lathe turning system. Int. J. Mach. Tools Manuf. 39(7), 1171–1183 (1999)CrossRefGoogle Scholar
  9. 9.
    S. Ito, D. lijima, A. Hayashi, Precision turning on a desk - Micro turning system. Paper presented at the 4th Euspen international conference, Glasgow, 2004Google Scholar
  10. 10.
    E. Kussul, T. Baidyk, L. Ruiz-Huerta, A. Caballero-Ruiz, G. Velasco, L. Kasatkina, Development of micromachine tool prototypes for microfactories. J. Micromech. Microeng. 12(6), 795–812 (2002)CrossRefGoogle Scholar
  11. 11.
    http://www.nanowave.co.jp. Accessed July 2013
  12. 12.
  13. 13.
    T.I. Ogedengbe, A contribution to the design and operation of a micro milling machine. PhD thesis, School of Mechanical, Aerospace and Civil Engineering, University of Manchester, UK, 2010Google Scholar
  14. 14.
    N. Kawahara, T. Suto, T. Hirano, Y. Ishikawa, T. Kitahara, N. Ooyama, T. Ataka, Microfactories; new applications of micromachine technology to the manufacture of small products. Microsyst. Technol. 3(2), 37–41 (1997)CrossRefGoogle Scholar
  15. 15.
    S. Mekid, A. Gordon P. Nicholson, Challenges and rationale in the design of miniaturised machine tool. Paper presented at the international MATADOR conference, UMIST, Manchester, 2004Google Scholar
  16. 16.
    L. Alting, F. Kimura, H.N. Hansen, G. Bissacco, Micro engineering. Ann CIRP 52(2), 635–657 (2003)CrossRefGoogle Scholar
  17. 17.
    A. Slocum, M. Basaran, R. Cortesi, A.J. Hart, Linear motion carriage with aerostatic bearings preloaded by inclined iron core linear electric motor. Precis. Eng. 27(4), 382–394 (2003)CrossRefGoogle Scholar
  18. 18.
    S. Mekid, High precision linear slide. part 1: design and construction. Int. J. Mach. Tools Manuf. 40(7), 1039–1050 (2000)Google Scholar
  19. 19.
    R. Yang, M. Jouaneh, R. Schweizer, Design and characterization of a low-profile micropositioning stage. Precis. Eng. 18(1), 20–29 (1996)CrossRefGoogle Scholar
  20. 20.
    S. Mekid, M. Bonis, Conceptual design and study of high precision translational stages: application to an optical delay line. Precis. Eng. 21(1), 29–35 (1997)CrossRefGoogle Scholar
  21. 21.
    P. Gao, S.M. Swei, Z. Yuan, A new piezodriven precision micropositioning stage utilizing flexure hinges. Nanotechnology 10(4), 394–398 (1999)CrossRefGoogle Scholar
  22. 22.
    A. Slocum, Precision Machine Design (Prentice Hall, Englewood Hill, 1992)Google Scholar
  23. 23.
    M.H. Smith, A.M. Annaswamy, A.H. Slocum, Adaptive control strategies for a precision machine tool axis. Precis. Eng. 17(03), 192–206 (1995)CrossRefGoogle Scholar
  24. 24.
    K.F. Ehmann, D. Bourell, M.L. Culpepper, T.J. Hodgson, T.R. Kurfess, M. Madou, K. Rajurkar R.E. Devor, WTEC Panel Report on International Assessment of Research and Development in Micromanufacturing (World Technology Evaluation Center (WTEC), Inc, Baltimore, 2005)Google Scholar
  25. 25.
    Z.H. Khan, J.M. Thiriet, D.G. Catalot, Wireless Network architecture for Diagnosis and Monitoring Applications. Paper presented at 6th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, 2009Google Scholar
  26. 26.
    S. Dye, Machine-to-Machine (M2M) Communications (2013), http://www.mobilein.com/M2M.htm. Accessed 2013
  27. 27.
    H. Chao, Y. Chen, J. Wu, Power saving for machine to machine communications in cellular networks. Paper presented at the IEEE GLOBECOM Workshops (GC Wkshps), 2011Google Scholar
  28. 28.
    W.S.N. Trimmer, Microrobots and micromechanical systems. Sens. Actuators 19(3), 267–287 (1989)CrossRefGoogle Scholar
  29. 29.
    W.C. Tang, T.C.H. Nguyen, R.T. Howe, Laterally driven polysilicon resonant microstructures. Sens. Actuators 20(1–2), 25–32 (1989)CrossRefGoogle Scholar
  30. 30.
    A.C. Okafor, Y.M. Ertekin, Derivation of machine tool error models and error compensation procedure for three axes vertical machining center using rigid body kinematics. Int. J. Mach. Tools Manuf 40(8), 1199–1213 (2000)CrossRefGoogle Scholar
  31. 31.
    W.Y. Fowlkes, C.M. Creveling, Engineering methods for robust product design using taguchi methods in technology and product development (Addison-Wesley Publishing Company, Boston, 1995)Google Scholar
  32. 32.
    Mishima N, Robustness evaluation of a miniaturized machine tool. Paper presented at the DETC, 25th design automation conference, Las Vegas, 1999Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMohammad Ali Jinnah University (MAJU)IslamabadPakistan
  2. 2.National University of Science and TechnologyIslamabadPakistan

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