title: A comparative study on improved Arrhenius-type and artificial neural network models to predict high-temperature flow behaviors in 20MnNiMo alloy.
abstract: The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173 ∼ 1473 K and strain rate range of 0.01 ∼ 10 s(-1). Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former, R and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of -39.99% ∼ 35.05% and -3.77% ∼ 16.74%. As for the former, only 16.3% of the test data set possesses η-values within ± 1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.
authors.affiliation: School of Material Science and Engineering, Chongqing University, Chongqing 400044, China.
authors.forename: Guo-zheng
authors.identifier: 0000-0002-1172-2696
authors.initials: GZ
authors.lastname: Quan
authors.affiliation: School of Material Science and Engineering, Chongqing University, Chongqing 400044, China.
authors.forename: Chun-tang
authors.initials: CT
authors.lastname: Yu
authors.affiliation: School of Material Science and Engineering, Chongqing University, Chongqing 400044, China.
authors.forename: Ying-ying
authors.initials: YY
authors.lastname: Liu
authors.affiliation: School of Material Science and Engineering, Chongqing University, Chongqing 400044, China.
authors.forename: Yu-feng
authors.initials: YF
authors.lastname: Xia
mesh_terms: D000465:Algorithms; D000497:Alloys / Q000737:chemistry; D019245:Compressive Strength; D003198:Computer Simulation; D004058:Diffusion; D055119:Elastic Modulus; D007700:Kinetics; D008345:Manganese / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D008982:Molybdenum / Q000737:chemistry*; D016571:Neural Networks, Computer; D009532:Nickel / Q000737:chemistry*; D044367:Phase Transition; D013718:Tensile Strength; D013814:Thermal Conductivity; D014783:Viscosity
chemical_list: D000497:Alloys; D008345:Manganese; D009532:Nickel; D008982:Molybdenum