Flow Stress Prediction of SiCp/Al Composites at Varying Strain Rates and Elevated Temperatures

• Published in: Journal of materials engineering and performance 2014-03, Vol.23 (3), p.1016-1027
• Main Authors: Yuan, Zhanwei, Li, Fuguo, Ji, Guoliang, Qiao, Huijuan, Li, Jiang
• Format: Article
• Language: English
• Subjects: Applied sciences; Characterization and Evaluation of Materials; Chemistry and Materials Science; Corrosion and Coatings; Dispersion hardening metals; Elasticity. Plasticity; Engineering Design; Exact sciences and technology; Materials Science; Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology; Metals. Metallurgy; Powder metallurgy. Composite materials; Production techniques; Quality Control; Reliability; Safety and Risk; Tribology
• ISSN: 1059-9495; 1544-1024
• DOI: 10.1007/s11665-013-0838-y

With isothermal compression tests in the Gleeble-3500 system, the hot deformation behaviors of SiCp/Al composite were studied at a wide range of temperatures from 623 K to 773 K, and strain rates ranging from 0.001 s −1 to 10 s −1 . Four different modeling methods such as the modified Zerilli-Armstrong model, the strain compensation Arrhenius-type model, the double multivariate nonlinear regression (DMNR) and the artificial neural model (ANN) were used to predict the flow stress. The suitability levels of these models were evaluated by contrasting both the correlation coefficient R C and the average absolute relative error. The results show that the predictions of these four models can adequately meet the accuracy requirement according to the experimental data of this composite. With the increasing of the numbers of determined material constants and the complexity of computing methods, the predictability of these four methods is enhanced. The deformation parameters in the selected ranges such as strain rate and temperature have non-ignorable effect on predicted results of the previous two methods, while they have slight influence on DMNR and ANN.