Overall evaluation of the surface integrity in two-dimensional ultrasonic surface rolling 7050 Al alloy

To improve the anti-fatigue performance and the service life of parts made of 7050 aluminium alloy, the surface integrity of the workpiece treated by two-dimensional ultrasonic surface rolling (TDUSR) was comprehensively evaluated, and the optimization methods of the process parameters were proposed...

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Veröffentlicht in:Journal of the Brazilian Society of Mechanical Sciences and Engineering 2020-06, Vol.42 (6), Article 325
Hauptverfasser: Zheng, Jianxin, Zhu, Lixin, Liu, Hongwei, Ren, Yuanchao
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Sprache:eng
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Zusammenfassung:To improve the anti-fatigue performance and the service life of parts made of 7050 aluminium alloy, the surface integrity of the workpiece treated by two-dimensional ultrasonic surface rolling (TDUSR) was comprehensively evaluated, and the optimization methods of the process parameters were proposed in this paper. The orthogonal experimental method was used to design an experiment program for TDUSR 7050 aluminium alloy. Based on the experimental results, the entropy weight method was used to make objectively weight for each quantifiable evaluation index. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by the grey relational analysis (GRA) was presented to evaluate the surface integrity. Quadratic regression analysis was applied to establish prediction models for the surface roughness, microhardness, residual stress and the close degree. The optimal process parameters for the surface integrity were obtained using planning solution based on the prediction models. The results show that the static load and the feed rate have significant effects on the surface integrity, and their contributions are 60.52% and 28.05%, respectively. The maximal close degree obtained by planning solution is bigger than that obtained by TOPSIS improved by GRA. The validation test result proves that the overall evaluation method of TOPSIS improved by GRA combined with prediction modelling method by quadratic regression analysis can provide comparatively better process parameters and obtain more desirable process responses.
ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-020-02423-1