Tailoring the microstructure and mechanical properties of (CrMnFeCoNi)100-xCx high-entropy alloys: Machine learning, experimental validation, and mathematical modeling

• Published in: Current opinion in solid state & materials science Vol. 27; no. 5
• Main Authors: Zamani, Mohammad Reza, Roostaei, Milad, Mirzadeh, Hamed, Malekan, Mehdi, Song, Min
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: High-entropy alloys; Machine learning; Mathematical modeling; Mechanical behavior; Recrystallization annealing; High-entropy alloys; Mechanical behavior; Mathematical modeling; Recrystallization annealing; Machine learning
• ISSN: 1359-0286
• DOI: 10.1016/j.cossms.2023.101105

[Display omitted] •Microstrucutre and mechanical properties of interstitial-containing HEAs during cold rolling and annealing were characterized.•Effects of processing parameters and chemical composition on the recrystallization and grain growth behaviors of (CrMnFeCoNi)100-xCx HEAs were quantified.•Grain size, carbide precipitation, and tensile yield stress for the model (CrMnFeCoNi)100-xCx HEAs were modeled.•Machine learning models, mathematical relationships, and equations for the contribution of each strengthening mechanism were proposed and verified by experimental work. As a common thermomechanical treatment route, “cold rolling and annealing” is widely used for the processing and grain refinement of interstitial-containing high-entropy alloys (HEAs). The interrelationship between the parameters of this process, the content of interstitial elements, and their interactions are outstanding challenges and areas of open discussion. Accordingly, the data-driven machine learning approach is a favorable choice for tuning the microstructure and mechanical properties, which needs to be systematically investigated. In the present work, these subjects were addressed in terms of correlating the thermomechanical processing parameters and chemical composition with the recrystallization and grain growth behaviors, grain size, carbide precipitation, and the resulting tensile yield stress for the model (CrMnFeCoNi)100-xCx HEAs. For this purpose, machine learning models based on adaptive neuro-fuzzy inference system (ANFIS), backpropagation artificial neural network (BP-ANN), and support network machine (SVM), as well as mathematical relationships and equations for the contribution of each strengthening mechanism were proposed and verified by extensive experimental work, which shed light on the design and prediction of the microstructure and properties of HEAs.