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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 768 - 778 (11page)
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An accurate processing map for a metal provides a means of attaining a desired microstructure and required shape throughthermo-mechanical processing. To construct such a map, the isothermal flow stress, σiso, is required. Conventionally, thenon-isothermal flow stress measured by experiment is corrected to σiso using whole-temperature-range linear interpolation(WRLI) or partial-temperature-range linear interpolation (PRLI). However, these approaches could incur significant errors ifthe non-isothermal flow stress exhibits a non-linear relationship with the temperature. In this study, an artificial neural network(ANN) model was applied to correct the non-isothermal flow stress in 10 wt% Cr steel, which exhibits a non-linear temperaturedependence within a target temperature range of 750–1250 °C. Processing maps were constructed using σiso corrected byapplying the WRLI, PRLI, and ANN approaches, respectively, and were then compared with the actual microstructures. TheWRLI approach produced the highest minimum error of σiso (17.2%) and over-predicted the shear-band formation. The PRLIapproach reasonably predicted the microstructural changes, but the minimum error for σiso (8.9%) was somewhat high. The ANNapproach not only realized the lowest minimum error of σiso (~ 0%), but also effectively predicted the microstructural changes.
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