Introductory Design Optimisation of a Machine Tool using a Virtual Machine Concept
Designing modern machine tools is a complex task. A
simulation tool to aid the design work, a virtual machine, has
therefore been developed in earlier work. The virtual machine
considers the interaction between the mechanics of the machine
(including structural flexibility) and the control system. This paper
exemplifies the usefulness of the virtual machine as a tool for product
development. An optimisation study is conducted aiming at
improving the existing design of a machine tool regarding weight and
manufacturing accuracy at maintained manufacturing speed. The
problem can be categorised as constrained multidisciplinary multiobjective
multivariable optimisation. Parameters of the control and
geometric quantities of the machine are used as design variables. This
results in a mix of continuous and discrete variables and an
optimisation approach using a genetic algorithm is therefore
deployed. The accuracy objective is evaluated according to
international standards. The complete systems model shows nondeterministic
behaviour. A strategy to handle this based on statistical
analysis is suggested. The weight of the main moving parts is reduced
by more than 30 per cent and the manufacturing accuracy is
improvement by more than 60 per cent compared to the original
design, with no reduction in manufacturing speed. It is also shown
that interaction effects exist between the mechanics and the control,
i.e. this improvement would most likely not been possible with a
conventional sequential design approach within the same time, cost
and general resource frame. This indicates the potential of the virtual
machine concept for contributing to improved efficiency of both
complex products and the development process for such products.
Companies incorporating such advanced simulation tools in their
product development could thus improve its own competitiveness as
well as contribute to improved resource efficiency of society at large.
[1] Thomke S.H., Experimentation matters: unlocking the potential of new
technologies for innovation,: Harvard Business School Press, Boston,
2003.
[2] Jönsson A., Wall J. & Broman G., "A virtual machine concept for realtime
simulation of machine tool dynamics", International Journal of
Machine Tools & Manufacture 45(7-8), 2005, pp.795-801.
[3] Van Brussel H., Sas P., Németh I., De Fonseca P. & Van den
Braembussche P., "Towards a mechatronic compiler". IEEE/ASME
Transactions on Mechatronics 6(1), 2001, pp. 90-105.
[4] Dierssen S., "Systemkopplung zur komponentenorientierten Simulation
digitaler Produkte", VDI-Fortschrittberichte, 20(358), VDI-Verlag
GmbH, D├╝sseldorf, 2002.
[5] Kreusch K., Verifikation numerischer Steuerungen an virtuellen
Werkzeugmaschinen, Berichte aus der Steuerungs- und
Regelungstechnik, Shaker Verlag, Aachen, 2002.
[6] Altintas Y., Brecher C., Weck M. & Witt S., "Virtual machine tool",
Annals of CIRP 54(2), 2005, pp. 651-674.
[7] Andersson J., "A survey of multiobjective optimization in engineering
design", Technical report LiTH-IKP-R-1097, Department of Mechanical
Engineering, Linköping University, Linköping, 2000.
[8] Ehrgott M. & Gandibleux X., "A survey and annotated bibliography of
multiobjective combinatorial optimization", OR Spektrum 22(4), 2000,
pp. 425-460.
[9] Schuëller G.I., "Computational stochastic mechanics - recent advances",
Computers and Structures 79(22-25), 2001, pp. 2225-2234.
[10] Moens D. & Vandepitte D., "A survey of non-probabilistic uncertainty
treatment in finite element analysis", Computer Methods in Applied
Mechanics and Engineering 194(12-16), 2005, pp.1527-1555.
[11] Summers D.A., Waterjetting technology, Spon Press, London, 1995.
[12] Wall J., Englund T. & Berghuvud A., "Identification and modelling of
structural dynamics characteristics of a water jet cutting machine", in:
Proceedings of the International Modal Analysis Conference - IMAC,
Dearborn, 26-29 January, 2004, pp. 138-147.
[13] Rosenkrantz W.A., Introduction to probability and statistics for
scientists and engineers, McGraw-Hill, New York, 1997.
[14] ISO 230-4:2005, "Test code for machine tools - Part 4: Circular tests for
numerically controlled machine tools".
[15] Gen M. & Cheng R., Genetic algorithms & engineering optimization,
John Wiley & Sons, New York, 2000.
[16] Coello Coello C.A., "Theoretical and numerical constraint-handling
techniques used with evolutionary algorithms: a survey of the state of
the art". Computer Methods in Applied Mechanics and Engineering
191(11-12), 2002, pp. 1245-1287.
[1] Thomke S.H., Experimentation matters: unlocking the potential of new
technologies for innovation,: Harvard Business School Press, Boston,
2003.
[2] Jönsson A., Wall J. & Broman G., "A virtual machine concept for realtime
simulation of machine tool dynamics", International Journal of
Machine Tools & Manufacture 45(7-8), 2005, pp.795-801.
[3] Van Brussel H., Sas P., Németh I., De Fonseca P. & Van den
Braembussche P., "Towards a mechatronic compiler". IEEE/ASME
Transactions on Mechatronics 6(1), 2001, pp. 90-105.
[4] Dierssen S., "Systemkopplung zur komponentenorientierten Simulation
digitaler Produkte", VDI-Fortschrittberichte, 20(358), VDI-Verlag
GmbH, D├╝sseldorf, 2002.
[5] Kreusch K., Verifikation numerischer Steuerungen an virtuellen
Werkzeugmaschinen, Berichte aus der Steuerungs- und
Regelungstechnik, Shaker Verlag, Aachen, 2002.
[6] Altintas Y., Brecher C., Weck M. & Witt S., "Virtual machine tool",
Annals of CIRP 54(2), 2005, pp. 651-674.
[7] Andersson J., "A survey of multiobjective optimization in engineering
design", Technical report LiTH-IKP-R-1097, Department of Mechanical
Engineering, Linköping University, Linköping, 2000.
[8] Ehrgott M. & Gandibleux X., "A survey and annotated bibliography of
multiobjective combinatorial optimization", OR Spektrum 22(4), 2000,
pp. 425-460.
[9] Schuëller G.I., "Computational stochastic mechanics - recent advances",
Computers and Structures 79(22-25), 2001, pp. 2225-2234.
[10] Moens D. & Vandepitte D., "A survey of non-probabilistic uncertainty
treatment in finite element analysis", Computer Methods in Applied
Mechanics and Engineering 194(12-16), 2005, pp.1527-1555.
[11] Summers D.A., Waterjetting technology, Spon Press, London, 1995.
[12] Wall J., Englund T. & Berghuvud A., "Identification and modelling of
structural dynamics characteristics of a water jet cutting machine", in:
Proceedings of the International Modal Analysis Conference - IMAC,
Dearborn, 26-29 January, 2004, pp. 138-147.
[13] Rosenkrantz W.A., Introduction to probability and statistics for
scientists and engineers, McGraw-Hill, New York, 1997.
[14] ISO 230-4:2005, "Test code for machine tools - Part 4: Circular tests for
numerically controlled machine tools".
[15] Gen M. & Cheng R., Genetic algorithms & engineering optimization,
John Wiley & Sons, New York, 2000.
[16] Coello Coello C.A., "Theoretical and numerical constraint-handling
techniques used with evolutionary algorithms: a survey of the state of
the art". Computer Methods in Applied Mechanics and Engineering
191(11-12), 2002, pp. 1245-1287.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52402", author = "Johan Wall and Johan Fredin and Anders Jönsson and Göran Broman", title = "Introductory Design Optimisation of a Machine Tool using a Virtual Machine Concept", abstract = "Designing modern machine tools is a complex task. A
simulation tool to aid the design work, a virtual machine, has
therefore been developed in earlier work. The virtual machine
considers the interaction between the mechanics of the machine
(including structural flexibility) and the control system. This paper
exemplifies the usefulness of the virtual machine as a tool for product
development. An optimisation study is conducted aiming at
improving the existing design of a machine tool regarding weight and
manufacturing accuracy at maintained manufacturing speed. The
problem can be categorised as constrained multidisciplinary multiobjective
multivariable optimisation. Parameters of the control and
geometric quantities of the machine are used as design variables. This
results in a mix of continuous and discrete variables and an
optimisation approach using a genetic algorithm is therefore
deployed. The accuracy objective is evaluated according to
international standards. The complete systems model shows nondeterministic
behaviour. A strategy to handle this based on statistical
analysis is suggested. The weight of the main moving parts is reduced
by more than 30 per cent and the manufacturing accuracy is
improvement by more than 60 per cent compared to the original
design, with no reduction in manufacturing speed. It is also shown
that interaction effects exist between the mechanics and the control,
i.e. this improvement would most likely not been possible with a
conventional sequential design approach within the same time, cost
and general resource frame. This indicates the potential of the virtual
machine concept for contributing to improved efficiency of both
complex products and the development process for such products.
Companies incorporating such advanced simulation tools in their
product development could thus improve its own competitiveness as
well as contribute to improved resource efficiency of society at large.", keywords = "Machine tools, Mechatronics, Non-deterministic,Optimisation, Product development, Virtual machine", volume = "5", number = "11", pages = "2209-6", }