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RISEvaluation of Machine Learning Models for Predicting the Hot Deformation Flow Stress of Sintered Al–Zn–Mg Alloy
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: In predicting flow stress, machine learning (ML) offers significant advantages by leveraging data-driven approaches, enhancing material design, and accurately forecasting material performance. Thus, the present study employs ...
Microstructure Modeling of Dynamically Recrystallized Grain Size of Sintered Al–4 wt % B4C Composite During Hot Upsetting
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Grain size control of any engineering metal is very important in the hot upsetting process. Generally, the grain size directly controls the mechanical properties and performance of the material. Al–B4C composite finds ...