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Fatigue Life Prediction Method for Self-Piercing Rivets Considering Crack Propagation
Technical Paper
2019-01-0531
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper describes a numerical prediction method for fatigue strength of Self Piercing Rivets (SPRs) using fracture mechanics. Recently, high strength steels and non-ferrous metals have been adopted to light weight automotive bodies. Various types of joining are proposed for multi-material bodies. It is important to predict the fatigue life of these joints using numerical simulation. However, the fatigue strength of these joints is related to sheet thickness, base materials, and loading conditions. Therefore, a large number of coupon tests are necessary to determine the S-N curve for the fatigue life prediction of joints in the automotive body. To reduce the amount of coupon testing, numerical simulation will be an efficient method in obtaining the S-N curve of these joints. The fatigue fracture process consists of two stages, crack initiation and crack growth. There are many studies about crack growth estimation methods using stress intensity factor. However, they cannot predict the crack initiation life. On the other hand, the fatigue life prediction method using the stress intensity factor based on Re-tensile Plastic zone Generating Load (RPG Load) can give us not only crack growth life but also the crack initiation life. The efficiency of the fatigue life prediction method based on RPG load has been reported for welding. However, no prediction methods based on RPG load have been reported for mechanical joints. In this paper, a fatigue life prediction method enhanced for mechanical joints based on RPG load is proposed. Numerical results of SPR are compared with the S-N curves obtained from fatigue tests, and the proposed methodology is considered effective and efficient for predicting S-N curves.
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Citation
Kawamura, H. and Cheng, M., "Fatigue Life Prediction Method for Self-Piercing Rivets Considering Crack Propagation," SAE Technical Paper 2019-01-0531, 2019, https://doi.org/10.4271/2019-01-0531.Data Sets - Support Documents
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