Characterization of grain growth behaviors by BP-ANN and Sellars models for nickle-base superalloy and their comparisons

In order to deeply understand the grain growth behaviors of Ni80A superalloy,a series of grain growth experiments were conducted at holding temperatures ranging from 1223 to 1423 K and holding time ranging from 0 to 3600 s.A back-propagation artificial neural network(BP-ANN)model and a Sellars model were solved based on the experimental data.The prediction and generalization capabilities of these two models were evaluated and compared on the basis of four statistical indicators.The results show that the solved BP-ANN model has better performance as it has higher correlation coefficient(r),lower average absolute relative error(AARE),lower absolute values of mean value(μ)and standard deviation(ω).Eventually,a response surface of average grain size to holding temperature and holding time is constructed based on the data expanded by the solved BP-ANN model,and the grain growth behaviors are described.

Correspondence between low-energy twin boundary density and thermal-plastic deformation parameters in nickel-based superalloy

To deeply understand and even describe the evolutions of the low-energy twin boundary density(BLDΣ3n)in a thermal-plastic deformation process,an improved twin density model as a function of average grain size and stored energy is developed.For Nimonic 80A superalloy,the model is solved based on the EBSD statistical results of grain size and BLDΣ3n in the specimens compressed at temperatures of 1273−1423 K and strain rates of 0.001−10 s−1.The corresponding relationships of BLDΣ3n with stored energy and grain size varying with temperature and strain rate are clarified by the superimposed contour plot maps.It is summarized that BLDΣ3n increases with increasing stored energy and decreasing grain size,and higher BLDΣ3n with finer grains corresponds with lower temperatures and higher strain rates.Such relationships are described by the improved twin density model,and the prediction tolerance of the solved model is limited in 2.8%.

Artificial intelligence model of complicated flow behaviors for Ti-13Nb-13Zr alloy and relevant applications

The comprehensive nonlinear flow behaviors of a ductile alloy play a significant role in the numerical analysis of its forming process. The accurate characterization of as-forged Ti-13 Nb-13 Zr alloy was conducted by an improved intelligent algorithm, GA-SVR, the combination of genetic algorithm(GA) and support vector regression(SVR). The GA-SVR model learns from a training dataset and then is verified by a test dataset. As for the generalization ability of the solved GA-SVR model, no matter in β phase temperature range or(α+β) phase temperature range, the correlation coefficient R-values are always larger than 0.9999, and the AARE-values are always lower than 0.18%. The solved GA-SVR model accurately tracks the highly-nonlinear flow behaviors of Ti-13 Nb-13 Zr alloy. The stress-strain data expanded by this model are input into finite element solver, and the computation accuracy is improved.

An approach for evaluating fire resistance of high strength Q460 steel columns

To develop a methodology for evaluating fire resistance of high strength Q460 steel columns, the load bearing capacity of high strength Q460 steel columns is investigated. The current approach of evaluating load bearing capacity of mild steel columns at room temperature is extended to high strength Q460 steel columns with due consideration to high temperature properties of high strength Q460 steel. The critical temperature of high strength Q460 steel column is presented and compared with mild steel columns. The proposed approach was validated by comparing the predicted load capacity with that evaluated through finite element analysis and test results. In addition, parametric studies were carried out by employing the proposed approach to study the effect of residual stress and geometrical imperfections. Results from parametric studies show that, only for a long column （slenderness higher than 75）, the magnitude and distribution mode of residual stress have little influence on ultimate load bearing capacity of high strength Q460 steel columns, but the geometrical imperfections have significant influence on any columns. At a certain slenderness ratio, the stability factor first decreases and then increases with temperature rise.