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Article type: Research Article
Authors: Dong, Xiangweia; b; * | Liu, G.R.b | Li, Zenglianga | Zeng, W.b; c
Affiliations: [a] College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao, Shandong, China | [b] CEAS – School of Aerospace Systems, University of Cincinnati, Cincinnati, OH, USA | [c] Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Correspondence: [*] Corresponding author: Xiangwei Dong, College of Mechanical and Electronic Engineering, China University of Petroleum (East China), 66 Changjiang Rd, Huangdao District, Qingdao, Shandong, China. E-mail: [email protected].
Abstract: Shot peening is a cold working process used mainly to improve the fatigue life of metallic components. The complexity of the process makes it difficult and costly to evaluate the mechanism through experiment. Therefore, the purpose of this study is to provide an numerical model for simulating this process. Smoothed particle hydrodynamics (SPH), which is a meshfree particle method, is employed. SPH formulations are presented for conducting dynamic elastic-plastic analysis. The target material is discretized into a set of SPH particles, and the shot is modeled as a rigid body. Contact algorithm is used to calculate the contact force between target and shots. In order to simulate a large number of shots, a particle-based periodic cell model is proposed. Then, the model is applied to simulate shot peening process for both single impact and multiple impacts. Coverage percentage is estimated by evaluating the plastic deformation of target material. Comparisons of results with theoretical model for coverage assessment show that the proposed SPH model can help predicting the shot peening process. The present work extends the application of SPH method in modeling the shot peening, which may be helpful in optimization of the treatment.
Keywords: Smoothed particle hydrodynamics, meshfree method, dynamic elastic-plastic analysis, mutiple shots, surface coverage
DOI: 10.3233/JCM-170764
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 17, no. 4, pp. 799-825, 2017
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