Abstract
Materials for aerospace industry such as aluminium alloys are of prime use in a variety of components. Performance and reliability of these components and structures mostly lie down on the fatigue resistance among other structural characteristics. Shot peening processing is widely employed to improve the fatigue properties. However, proper selection and control of peening factors (parameters) is needed to ensure that peening effects became beneficial rather than detrimental. The present study focuses on finding optimal peening parameters by considering multiple performance characteristics using grey fuzzy methodology. Adaptive neuro-fuzzy inference system (ANFIS) approach was used to investigate the effects of the input parameters, namely, shot type, coverage and incidence angle on the performance parameters, i.e. residual stresses, work hardening and stress concentrations. A confirmation test in terms of fatigue resistance was also carried out to validate the results from which and improvement was obtained.
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Acknowledgements
The authors would like to thank the Secretary of Public Education of Mexico/TecNM for the financial support. The University of Sheffield, U.K., for offering facilities to implement the project is also gratefully acknowledged.
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Solis-Cordova, J., Roblero-Aguilar, S., Almanza-Ortega, N., Solis-Romero, J. (2019). Grey-Fuzzy Approach to Support the Optimisation of the Shot Peening Process. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_30
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DOI: https://doi.org/10.1007/978-3-030-33749-0_30
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