Journal
MATERIALS
Volume 15, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/ma15113788
Keywords
AA5005 alloy; high temperature; Arrhenius-type; flow stress; low strain rate; BP-ANN; constitutive relationship
Categories
Funding
- Korea Institute of Energy Technology Evaluation and Planning (KETEP) - Korea government (MOTIE) [20206310100050]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20206310100050] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Conducting research on the flow behavior of lightweight materials under specified work conditions is crucial for achieving energy-saving goals. In this study, hot isothermal tensile tests and machine learning methods were used to analyze the flow behavior of aluminum alloy AA5005. The results showed that machine learning methods have better performance in regressing and predicting flow stress.
To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003-0.03 s(-1) and temperature range 633-773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R-2), average absolute relative error (AARE), and relative error (delta) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R-2, lower AARE, and more concentrative delta value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.
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