Abstract
This article presents the problem of determining the mountability level of the assembly station using an artificial neural network (ANN). The results of ANN modelling were compared with the results of experimental research and classical mathematical modelling. It was found that the error in predicting the mountability level using the artificial neural network is about two-fold lower than in the case of the error determined by classical mathematical modelling. Although the neural network ensures a lower prediction error, to obtain a good prediction it is necessary to conduct many experiments in the whole workspace of the robots to build a training set. Despite the worst prediction, a mathematical model of the mountability level only requires an analytical description of the kinematic structure of the assembly robot, so in industrial applications this is preferred due to the lower labour requirement.
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Kluz, R.: Theoretical and experimental investigations of mountability of cylindrical parts. Technol. Autom. Assembly 1, 6–9 (2008)
Chen, H., Zhang, G., Wang, J., Eakins, W., Fuhlbrigge, T.: Flexible assembly automation using industrial robots. In: Proceedings of IEEE International Conference on Technologies for Practical Robot Applications, pp. 46–51. IEEE, Woburn (2008)
Makris, S., Michalos, G., Eytan, A., Chryssolouris, G.: Cooperating robots for reconfigurable assembly operations: review and challenges. Procedia CIRP 3, 346–351 (2012)
Tsarouchi, P., Makris, S., Michalos, G., Stefos, M., Fourtakas, K., Kalsoukalas, K., Kontrovrakis, D., Chryssolouris, G.: Robotized assembly process using dual arm robot. Procedia CIRP 23, 47–52 (2014)
Wang, L., Mohammed, A., Onori, M.: Remote robotic assembly guided by 3D models linking to a real robot. CIRP Ann. 63(1), 1–4 (2014)
Krüger, J., Schreck, G., Surdilovic, D.: Dual arm robot for flexible and cooperative assembly. CIRP Ann. 60(1), 5–8 (2011)
Cho, N., Tu, J.F.: Quantitative circularity tolerance analysis and design for 2D precision assemblies. Int. J. Mach. Tools Manuf 42(13), 1391–1401 (2002)
Kluz, R.: Mountability of sleeve joining realised by using assembly robots. Technol. Autom. Assembly 2, 17–20 (2007)
Kluz, R.: Determination of the optimum configuration of robotized assembly station. Arch. Mech. Technol. Mater. 29, 113–122 (2009)
Yegnanarayana, B.: Artificial Neural Networks. Prentice-Hall, New Delhi (2006)
Patterson, D.W.: Artificial Neural Networks—Theory and Applications. Prentice-Hall, Englewood Cliffs (1998)
StatSoft Inc.: Manual of STATISTICA Neural Networks Software. StatSoft Inc., Tulsa (1998)
Trzepieciński, T., Lemu, H.G.: Application of genetic algorithms to optimize neural networks for selected tribological tests. J. Mech. Eng. Autom. 2(2), 69–76 (2012)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley, Reading (1991)
Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer Verlag, Berlin Heidelberg (2008)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. John Wiley & Sons Inc., New York (2000)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. John Wiley & Sons. Inc., New York (1997)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)
Cilimkovic, M.: Neural networks and back propagation algorithm. http://www.dataminingmasters.com/uploads/studentProjects/NeuralNetworks.pdf. Accessed 22 Feb 2018
Myers, R.H., Montgomery, D.C., Anderson, C.M.: Response Surface Methodology Process and Product Optimization using Designed Experiments. John Wiley and Sons Inc., New York (2009)
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Kluz, R., Antosz, K., Trzepiecinski, T. (2019). Forecasting the Mountability Level of a Robotized Assembly Station. In: Burduk, A., Chlebus, E., Nowakowski, T., Tubis, A. (eds) Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-319-97490-3_17
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DOI: https://doi.org/10.1007/978-3-319-97490-3_17
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