Abstract
This study presents a comprehensive simulation model for the ultrasonic shot peening (USP) process applied to aluminum A380 alloy, a material widely used in the automotive industry. The model accurately predicts deformation and stress distribution on the alloy’s surface under varying shot parameters, including shot material and initial velocity. The results indicate that the maximum von Mises stress (VMS) produced by the USP process should lie between the yield strength and the ultimate tensile strength (UTS) of the aluminum A380 alloy to avoid excessive plastic deformation and initiate beneficial crack propagation. The study found that the required initial velocity for the stainless steel shot is 60–120 ms−1, while the tungsten carbide shot only requires an initial velocity of 60–90 ms−1 to produce hydrocompressive residual stress on the surface layer of the aluminum A380 alloy at optimum conditions. The simulation model developed in this study provides a valuable tool for optimizing the USP process parameters for aluminum A380 alloy, enhancing the performance of automotive parts made from this material, and reducing the resources required for experimental testing. Future work should focus on validating the simulation model against experimental results and exploring the effects of other process parameters on the alloy’s mechanical properties and ductility.
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Acknowledgements
The authors would like to express their deepest gratitude to the Ministry of Higher Education (MoHE) and University Malaysia Pahang for their generous financial support, which made this research possible. The funding provided through the Fundamental Research Scheme (FGRS) FRGS/1/2022/TK10/UMP/02/67 and the University Malaysia Pahang's Fundamental Research Grant RDU220317 was instrumental in facilitating the various aspects of this study, from the acquisition of materials to the conduction of experiments and analysis of data.
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Ruben, S., Yasin, M.R.M. (2024). Simulation on Effect of Ultrasonic Shot Peening Velocity on VMS of Aluminum A380 Die-Casting Alloy. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_38
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