Simulation and Optimization of Mechanisms made of Micro-molded Components
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Simulation and Optimization of Mechanisms made of Micro-molded Components

Authors: Albert Albers, Pablo Enrique Leslabay

Abstract:

The Institute of Product Development is dealing with the development, design and dimensioning of micro components and systems as a member of the Collaborative Research Centre 499 “Design, Production and Quality Assurance of Molded micro components made of Metallic and Ceramic Materials". Because of technological restrictions in the miniaturization of conventional manufacturing techniques, shape and material deviations cannot be scaled down in the same proportion as the micro parts, rendering components with relatively wide tolerance fields. Systems that include such components should be designed with this particularity in mind, often requiring large clearance. On the end, the output of such systems results variable and prone to dynamical instability. To save production time and resources, every study of these effects should happen early in the product development process and base on computer simulation to avoid costly prototypes. A suitable method is proposed here and exemplary applied to a micro technology demonstrator developed by the CRC499. It consists of a one stage planetary gear train in a sun-planet-ring configuration, with input through the sun gear and output through the carrier. The simulation procedure relies on ordinary Multi Body Simulation methods and subsequently adds other techniques to further investigate details of the system-s behavior and to predict its response. The selection of the relevant parameters and output functions followed the engineering standards for regular sized gear trains. The first step is to quantify the variability and to reveal the most critical points of the system, performed through a whole-mechanism Sensitivity Analysis. Due to the lack of previous knowledge about the system-s behavior, different DOE methods involving small and large amount of experiments were selected to perform the SA. In this particular case the parameter space can be divided into two well defined groups, one of them containing the gear-s profile information and the other the components- spatial location. This has been exploited to explore the different DOE techniques more promptly. A reduced set of parameters is derived for further investigation and to feed the final optimization process, whether as optimization parameters or as external perturbation collective. The 10 most relevant perturbation factors and 4 to 6 prospective variable parameters are considered in a new, simplified model. All of the parameters are affected by the mentioned production variability. The objective functions of interest are based on scalar output-s variability measures, so the problem becomes an optimization under robustness and reliability constrains. The study shows an initial step on the development path of a method to design and optimize complex micro mechanisms composed of wide tolerated elements accounting for the robustness and reliability of the systems- output.

Keywords: Micro molded components, Optimization, Robustness und Reliability, Simulation

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071786

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References:


[1] Albers, A.; et all, An integrated Approach for Validating Micro Mechanical Systems based on Simulation and Test, MicroSystem Technologies, Springer, 2008.
[2] Albers, A.; et all, Dealing with Uncertainty of Micro Gears - Integration of Dimensional Measurement, Virtual and Physical Testing, ASME International Mechanical Engineering Congress & Exposition, 2008.
[3] DIN 867:1986-02, Basic rack tooth profiles for involute teeth of cylindrical gears for general engineering and heavy engineering, Beuth Verlag, 1986.
[4] DIN 3960:1987-03, Definitions, parameters and equations for involute cylindrical gears and gear pairs, Beuth Verlag, 1987.
[5] DIN 3990 series, Calculation of load capacity of cylindrical gears, Beuth Verlag, 1987.
[6] Bausch, T. et all, Moderne Zahnradfertigung, 2. Auflage, Expert Verlag, 1994.
[7] Roth, K., Zahnradtechnik Band II, Springer Verlag, 1989.
[8] DIN 58400:1984-06, Basic rack for involute teeth of cylindrical gears for fine mechanics, Beuth Verlag, 1984.
[9] DIN 58425 series, Gears with round flanks for fine mechanics, Beuth Verlag, 1980.
[10] Menz, W., et all, Mikrosystemtechnik f├╝r Ingenieure, 3. Auflage, Wiley- VCH, 2005.
[11] Houbolt, J.C., A recurrence matrix solution for the dynamic response of elastic aircraft, J. Aeron. Sci. Vol. 17, pp. 540-550, 1950.
[12] Albers, A.; Enkler, H.-G.; Leslabay, P., On the Simulation of Molded Micro Components and Systems, MicroSystem Technologies, Springer, 2008.
[13] Lagarias, J.C., et all, Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions, SIAM Journal of Optimization, Vol. 9 Number 1, pp. 112-147, 1998.
[14] Sacks, J., et all, Design and Analysis of Computer Experiments, Statistical Science, Vol. 4 Number 4, 1989.
[15] Box, G. A. F., W. G. Hunter, and J. S. Hunter, Statistics for Experimenters, Wiley, 1978.
[16] Montgomery, D. C., Design and Analysis of Experiments, Wiley, 2001.
[17] Plackett, R.L., Burman, J.P., The Design of Optimal Multifactorial Experiments, Biometrika Vol. 33, 1946.
[18] Ryser, H. J., Combinatorial Mathematics, John Wiley & Sons, 1963.
[19] Box, G. E. P., Draper, N.R., Empirical Model Building and Response Surfaces, John Wiley & Sons, New York, 1987.
[20] Jurecka, F., Robust design optimization based on metamodeling techniques, Dissertation, TU M├╝nchen, 2007.
[21] Box, M.J., Draper, N.R., Factorial designs, the |xtx| criterion, and some related matters, Technometrics, Vol. 13, pp. 731-742, 1971.
[22] Atkinson, A. C., and A. N. Donev, Optimum Experimental Designs, Oxford University Press, 1992.
[23] Doltsinis, I., Kang, Z., Robust design of structures using optimization methods, Comput. Methods Appl. Mech. Engrg. Vol 193, pp. 2221-2237, 2004.
[24] Unger, J., Roos, D., Investigation and benchmark of algorithms for reliability analysis, Weimarer Optimierungs- und Stochastiktage 1.0, 2004.
[25] Spall, J. C., Introduction to Stochastic search and Optimization, Wiley, 2003.
[26] Jones, D.R., Schonlau, M., Welch, W.J., Efficient global optimization of expensive black-box functions, Journal of Global Optimization, Vol. 13-4, pp455-492, 1998.