Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys

Abstract

The potentials of artificial neural network (ANN) modelling as a potent machine learning approach for investigating the hot deformation behaviour of high-entropy alloys (HEAs) and multi-principal element alloys during thermomechanical processing are assessed and reviewed. Flow stress of CoCrFeNiMn (FCC Cantor alloy), HfNbTaTiZr (BCC refractory alloy), AlCoCuFeNi, and Al xCoCrFeNi alloys is accurately predicted based on the deformation temperature, strain rate, and strain. Moreover, in comparison with the limited experimental dataset, a significantly larger output dataset can be generated by ANN to gain valuable insights such as prediction of flow stress (and whole dynamic recovery/recrystallisation flow curves), elucidating the microstructural mechanisms such as dynamic precipitation reactions, and obtaining hot working parameters (e.g. deformation activation energy) for different ranges of deformation conditions.

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

“…In recent years, the rise of artificial intelligence and machine learning technologies has brought new possibilities for the assessment of the health of preschool children. Artificial Neural Networks (ANN), as a vital artificial intelligence method, have garnered widespread attention [3] . Neural network models possess robust nonlinear fitting capabilities and adaptive learning features.…”