Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network
Source abstract
Abstract
The metadynamic softening behaviors in 42CrMo steel were investigated by isothermal interrupted hot compression tests. Based on the experimental results, an efficient artificial neural network (ANN) model was developed to predict the flow stress and metadynamic softening fractions. The effects of deformation parameters on metadynamic softening behaviors in the hot deformed 42CrMo steel have been investigated by the experimental and predicted results from the developed ANN model. Results show that the effects of deformation parameters, such as strain rate and deformation temperature, on the softening fractions of metadynamic recrystallization are significant. However, the strain (beyond the peak strain) has little influence. A very good correlation between experimental and predicted results indicates that the excellent capability of the developed ANN model to predict the flow stress level and metadynamic softening, the metadynamic recrystallization behaviors were well evidenced.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metallurgy and Material Forming · Microstructure and Mechanical Properties of Steels · Metal Alloys Wear and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
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