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Probabilistic methodology for predicting the dispersionof residual stresses and Almen intensity considering shot peening process uncertainties

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Abstract

To ensure a high fatigue life and a reduced weight of automotive suspension system components, compressive residual stresses are commonly induced near the surface using many mechanical surface treatments. Among the most preferred techniques, shot peening process presents a high efficiency and a relative low cost. Nevertheless, the employment of such process is generally affected by many sources of variability. Indeed, the experimental residual stress measurements exhibit a significant variation from one component to another and even from different positions on the same component. Therefore, error bars are commonly used to quantify the variability of experimental residual stress measurements. Nevertheless, the majority of predictive approaches of residual stresses induced by shot peening do not consider the effect of the variability of shot peening process parameters. In this study, a probabilistic methodology is applied to evaluate the variability of the induced residual stress profile and the Almen intensity, regarding the scattering of the most significant shot peening process parameters. Furthermore, iso-probabilistic residual stress profile can be utilized to predict the shot peening residual stress profile with a specified probability of appearance.

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Authors and Affiliations

  1. Ecole Nationale d’Ingénieurs de Sousse, BP 264 Sousse Erriadh, Université de Sousse, Sousse, Tunisia

    A. Atig, R. Ben Sghaier, R. Seddik & R. Fathallah

  2. Institut Supérieur des Sciences Appliquées et de Technologie de Sousse, Rue Tahar Ben Achour Sousse, Université de Sousse, Sousse, Tunisia

    A. Atig & R. Ben Sghaier

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  1. A. Atig
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  2. R. Ben Sghaier
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  3. R. Seddik
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  4. R. Fathallah
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Correspondence to A. Atig.

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Atig, A., Ben Sghaier, R., Seddik, R. et al. Probabilistic methodology for predicting the dispersionof residual stresses and Almen intensity considering shot peening process uncertainties. Int J Adv Manuf Technol 94, 2125–2136 (2018). https://doi.org/10.1007/s00170-017-1033-3

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  • DOI: https://doi.org/10.1007/s00170-017-1033-3

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