Abstract
Double-Row Tapered Roller Bearings are mechanical systems widely used in vehicles for the transmission of high load and moderate rotation speeds.
These kinds of bearings are designed to withstand high contact stresses on their raceways, which are usually quantified using numerical methods such as the finite element method (FEM). This method has recently been widely used for designing mechanical systems, but has the disadvantage of requiring a high computational cost. The myriad of possible combinations of operating loads on the bearing (preload, radial load, axial load and torque) makes it much harder to calculate the distribution of these contact stresses. This paper shows the results of several regression models built using different Data Mining (DM) techniques that model and optimize the contact ratio obtained from the contact stresses in the outer raceway in Double-Row Tapered Roller Bearings. Firstly, a representative three-dimensional Finite Element (FE) model was generated according to the material properties, geometries and mechanical contacts of all parts which make up the bearing. Subsequently, a design of experiments (DoE) was performed considering four inputs (preload, radial load, axial load and torque), which were simulated in the FE model. Based on the contact stresses obtained from the FE simulations at different operating loads (inputs), a group of regression models (using linear regression (LR), quadratic regression (QR), isotonic regression (IR), Gaussian processes (GP), artificial neural networks (ANN), support vector machines (SVM) and regression trees (RT)) were built to predict the contact ratio which acts on the bearing. Finally, the best combination of operating loads were achieved by applying evolutionary optimization techniques based on Genetic Algorithms (GA) on the best regression models previously obtained. The optimization of the bearing was achieved when the radial loads obtained were the maximum value while the contact ratios were close to 25%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Harris, T.A., Kotzalas, M.N.: Essential concepts of bearing technology. CRC Press (2006)
Feng, Q., Prinja, N.K.: NAFEMS Benchmark Tests for Finite Element Modelling of Contact, Gapping and Sliding. NAFEMS Report R0081 (2001)
Satyanarayana, S., Melkote, S.N.: Finite element modeling of fixture-workpiece contacts: single contact modeling and experimental verification. International Journal of Machine Tools and Manufacture 44, 903–913 (2004)
Zhang, X.P., Ahmed, H., Yao, Z.: Multi-body contact modeling and statistical experimental validation for hub-bearing unit. Tribology International 36, 505–510 (2003)
Calvo-Rolle, J.L., Corchado, E.: A Bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)
Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2010)
Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.: Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16, 657–666 (2003)
Pandya, D.H., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Systems with Applications 40(10), 4137–4145 (2013)
Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5), 501–521 (2009)
Lostado, R., Martínez De Pisón, F.J., Pernía, A., Alba, F., Blanco, J.: Combining regression trees and the finite element method to define stress models of highly non-linear mechanical systems. J. Strain Analysis 44, 491–502 (2009)
Fisher, R.A.: The design of experiments (1935)
Montgomery, D.C.: Design and analysis of experiments. John Wiley & Sons (2008)
Team, R.C.: R: A language and environment for statistical computing. R Foundation for Statistical Computing (2005)
Martínez-de-Pisón, F.J., Lostado, R., Pernía, A., Fernández, R.: Optimising tension levelling process by means of genetic algorithms and finite element method. Ironmaking & Steelmaking 38, 45–52 (2011)
Lostado, R., Martínez-de-Pisón, F.J., Fernández, R., Fernández, J.: Using genetic algorithms to optimize the material behaviour model in finite element models of processes with cyclic loads. The Journal of Strain Analysis for Engineering Design 46(2), 143–159 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lostado-Lorza, R., Escribano-García, R., Fernández-Martínez, R., Illera-Cueva, M., Donald, B.J.M. (2014). Combination of the Finite Element Method and Data Mining Techniques to Design and Optimize Bearings. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-07995-0_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
Online ISBN: 978-3-319-07995-0
eBook Packages: EngineeringEngineering (R0)