Cognitive failure cluster enhancing the efficiency and the precision of the self-optimizing process model for bevel gear contact patterns

• Autorzy: Schmitt R. , Niggemann C. , Laass M.
• Języki publikacji: EN

Abstrakty

EN

The contact patterns of bevel gear sets are an important indicator for the acoustic quality of rear axle drives. The contact patterns are the result of complex interactions in the production process. This is due to many process steps, numerous influencing factors and interdependencies. In general, their effect on product variations is not fully comprehended. This impedes the design and control of the production process based on a holistic analytical model for new variants fulfilling the acoustic requirements. The approach with self-optimization is possible but can take a long time for the training of the artificial neural networks and the necessary iterations until a satisfying precision for the predicted process parameters is achieved. Also it can occur that the algorithm is not converging and therefore no satisfactory result is turned out at all. In this paper an approach is presented combining the flexibility of self-optimizing systems with the higher precision of delimited solution finders called the Cognitive Failure Cluster (CFC). The improvements provided by the clustering of the optimization program are evaluated regarding the training time and the precision of the result for a production lot of bevel gear sets.

Słowa kluczowe

EN

cognition; classification; self-optimization; gear testing; contact pattern

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2012
• Tom: Vol. 12, No. 1
• Strony: 55--65
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Schmitt R.

• Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Germany

autor

Niggemann C.

• Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Germany

autor

Laass M.

• Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Germany

Bibliografia

• [1] SCHUH G., KLOCKE F., BRECHER C., SCHMITT R., 2007, Excellence in Production. 1st edition, Apprimus Aachen
• [2] ORILSKI S., SCHUH G., 2007, Roadmapping for Competitiveness of High Wage Countries. Proc. of the XVIII ISPIM Conference: On Innovation for Growth – the Challenges for East and West, Warsaw
• [3] SCHMITT R., LAASS M., ISERMANN M, WAGELS C., Cognitive learning in self-optimization production systems, in: Proc. of MITIP 2011, Norwegian University of Science and Technology, Trondheim, Norway
• [4] SCHMITT R., ISERMANN M.; WAGELS C., MATUSCHEK N., 2010, Cognitive optimization of an automotive rear-axle drive production process. Jour. of Machine Engineering, 9/4/71-80
• [5] SCHMITT R., NIGGEMANN C., ISERMANN M., LAASS M., MATUSCHEK N., 2011, Cognition-based self- optimisation of an automotive rear axle drive production process. Jour. of Machine Engineering, 10/3/68-77
• [6] FRANK U., GIESE H., KLEIN F., OBERSCHELP O., SCHMIDT A., SCHULZ B., VÖCKING H., WITTING K. 2004, Selbstoptimierende Systeme im Maschinenbau. Definitionen und Konzepte. Paderborn: HNI Verlag
• [7] SOAR TECHNOLOGY. http://www.soartech.com, 17.11.2011.
• [8] JÄHNE B., 2005, Digital image Processing. 6th revised and extended edition, Springer Berlin
• [9] GONZALEZ R.C., WOODS R.E., 2001, Digital Image Processing, 2nd int. edition, Prentice Hall Int.
• [10] EPANECHNIKOV V.A., 1969, Non-parametric estimation of a multivariate probability density, Theory of Probability and its Applications, 14/153-158.
• [11] MACQUEEN J.B., (1967, Some Methods for classification and Analysis of Multivariate Observations, Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 281-297.