Physics

Mathematics

Solid mechanics

Mechanics

Motion (physics)

Isothermal process

Mathematical analysis

Interpolation (computer graphics)

Philosophy

Artificial intelligence

Composite material

Flow stress

Linear interpolation

Materials science

Range (aeronautics)

Stress (linguistics)

Flow (mathematics)

Computer science

Linguistics

Applied mathematics

Atmospheric temperature range

Artificial neural network

Microstructure

Polynomial

Thermodynamics

Metals and Materials International

An accurate processing map for a metal provides a means of attaining a desired microstructure and required shape through thermo-mechanical processing. To construct such a map, the isothermal flow stress, σiso, is required. Conventionally, the non-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 if the 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 temperature dependence within a target temperature range of 750–1250 °C. Processing maps were constructed using σiso corrected by applying the WRLI, PRLI, and ANN approaches, respectively, and were then compared with the actual microstructures. The WRLI approach produced the highest minimum error of σiso (17.2%) and over-predicted the shear-band formation. The PRLI approach reasonably predicted the microstructural changes, but the minimum error for σiso (8.9%) was somewhat high. The ANN approach not only realized the lowest minimum error of σiso (~ 0%), but also effectively predicted the microstructural changes.

Neural Network Approach to Construct a Processing Map from a Non-linear Stress–Temperature Relationship