A Comparative Study on Artificial Neural Network, Phenomenological-Based Constitutive and Modified Fields–Backofen Models to Predict Flow Stress in Ti-4Al-3V-2Mo-2Fe Alloy
Source abstract
Abstract
The flow curve was obtained from the superplastic tensile tests in the temperature range of 1033-1153 K and strain rate range of 0.001-0.1 s−1. Results showed that strain hardening (SH) was insensitive to deformation parameter variation but with the nature of material, while stable state (SS) and dynamic softening (DS) were significantly associated with a thermal activated process. The occurrence of secondary hardening and softening was attributed to discontinuous dynamic recrystallization. And the generation of necklace microstructure at grain boundaries was caused by bulging. Based on the experimental data, the artificial neural network (ANN), phenomenological-based constitutive (PBC) and modified Fields–Backofen (MFB) models were developed, and their predictability and adaptability for the description of the flow behavior during hot processing were evaluated by analyzing the employed standard statistical parameters. Comparatively, ANN model is capable of tracing the complex flow behavior including SH, SS and DS substages. Concerning PBC model, it can not only predict flow stress with satisfactory accuracy, but also represent the flow trend with the larger strain. MFB model adapts well to the flow curve with a broad SH region without DS effect.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metallurgy and Material Forming · High-Velocity Impact and Material Behavior · Microstructure and mechanical properties
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References (34)
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