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Joint Mechanism and Prediction of Strength for a Radial Knurling Connection of Assembled Camshaft Using a Subsequent Modeling Approach

Published June 25, 2018 by SAE International in United States
Joint Mechanism and Prediction of Strength for a Radial Knurling
                    Connection of Assembled Camshaft Using a Subsequent Modeling
                    Approach
Sector:
Citation: Zhang, P., Kou, S., Li, C., and Kou, Z., "Joint Mechanism and Prediction of Strength for a Radial Knurling Connection of Assembled Camshaft Using a Subsequent Modeling Approach," SAE Int. J. Engines 11(3):301-310, 2018, https://doi.org/10.4271/03-11-03-0020.
Language: English

Abstract:

Knurling joint applied in assembled camshaft has developed rapidly in recent years, which have exhibited great advantages against conventional joint methods in the aspects of automation, joint precision, thermal damage, noise, and near net shape forming. Both quality of assembly process and joint strength are the key requirements for manufacturing a reliable assembled camshaft. In this article, a finite element predictive approach including three subsequent models (knurling, press-fit and torsion strength) has been established. Johnson-Cook material model has been used to simulate the severe plastic deformation of the material. The residual stress field calculated from the knurling process was transferred as initial condition to the press-fit model to predict the press-fit load. The predicted press-fit load, torque strength and displacement of cam profile before failure were calculated. The torque strength of the joint was twice higher than that of a typical passenger vehicle requirement. The torque strength was significantly positive correlated to the press-fit load. Taking the knurling tool dimensions and feed amount as variables, the relationships between them and press-fit as well as joint strength were studied. The predicted press-fit and joining strength using the subsequent modeling ware validated by the experimental measurement with maximum errors less than 11%.