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

• Published in: Metals and materials international Vol. 25; no. 3; pp. 768 - 778
• Main Authors: Park, Chan Hee, Cha, Dojin, Kim, Minsoo, Reddy, N. S., Yeom, Jong-Taek
• Format: Journal Article
• Language: English
• Published: Seoul The Korean Institute of Metals and Materials 01.05.2019; Springer Nature B.V; 대한금속·재료학회
• Subjects: Artificial neural networks; Characterization and Evaluation of Materials; Chemistry and Materials Science; Chromium steels; Engineering Thermodynamics; Flow mapping; Heat and Mass Transfer; Heat treating; Interpolation; Isothermal flow; Machines; Magnetic Materials; Magnetism; Manufacturing; Materials Science; Metallic Materials; Microstructure; Neural networks; Process mapping; Processes; Solid Mechanics; Temperature dependence; Thermomechanical treatment; Yield strength; 재료공학; Computer modelling; Metallic alloys; Microstructure; Stress–strain measurements; Thermomechanical; Processing
• ISSN: 1598-9623; 2005-4149
• DOI: 10.1007/s12540-018-00225-8

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, σ iso , is required. Conventionally, the non-isothermal flow stress measured by experiment is corrected to σ iso 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 σ iso 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 σ iso (17.2%) and over-predicted the shear-band formation. The PRLI approach reasonably predicted the microstructural changes, but the minimum error for σ iso (8.9%) was somewhat high. The ANN approach not only realized the lowest minimum error of σ iso (~ 0%), but also effectively predicted the microstructural changes.