Study On Prediction Methods Of Microstructure And Properties Of Alloys By Machine Learning

The fourth paradigm of scientific research declares the advent of the era of data-intensive scientific research methods.The development of machine learning and artificial intelligence technology has provided a new way for increasingly complicated material design.Database and machine learning technologies can reveal the relationship between the design process and the macroscopic properties of materials.It can also optimize the experimental process and accelerate material design,instead of relying on the scientist intuition and a large number of "trial and error" experiences.Based on database and machine learning technology,the data-driven prediction methods for the structure and property of alloy materials are developed.By analyzing the characteristics of material experimental data,simulation data and industrial production data,we explore the construction' method of material scientific database;meanwhile,we carry out data mining and machine learning algorithms,such as correlation analysis,feature selection,classification,regression,ensemble learning and deep learning,to optimize the alloy design methods on the basis of experimental data,calculation simulation data and industrial production data,respectively.The details are as follows.1.By analyzing the characteristics of material science data,data definition methods,knowledge definition and representation methods,and data exchange mechanisms are established for material experimental characterization data,computational simulation data,and industrial production data.These methods provide basic support for the analysis and utilization of materials science data,and also serve as database construction and management techniques for massive heterogeneous data in machine learning.2.Based on the experimental data,we provide a machine learning approach to predict misfit using relevant material descriptors including the chemical composition,dendrite information and measurement temperature and so on.We perform support vector regression,sequential minimal optimization regression and multilayer perceptron algorithms with linear and poly kernels on experimental dataset for appropriate model selecting,and multilayer perceptron model works well for its distinguished prediction performance with high correlation coefficient and low error values.The approach is validated by comparing the predicted lattice misfit with a widely used empirical formula and experimental observation with respect to prediction accuracy.3.Based on the simulation data,we develop a fast and accurate method to predict quasi phase equilibrium via machine learning.Taking the isothermal solidification process of Al-Cu-Mg alloy as an example,the artificial neural network method is used to establish the accurate quantitative relationship model for quasi-equilibrium components of the precipitated phase and liquid phase.The machine learning model only takes 1/1000 of the calculation time comparing with solving the quasi-phase equilibrium equation by the least square method.Its high accuracy and fast speed demonstrate that the neural network model can obtain the quasi phase equilibrium data conveniently in phase field model for multicomponent alloys.4.Based on the industrial data,an effective strategy combining machine learning with multiscale calculation is promoted to construct tensile strength model for pearlitic steel wires.We transform the feature space by coupling of grain growth,dynamic recrystallization,temperature field and cooling phase transition calculations,mapping process space to microscopic structure space.The proeutectoid ferrite content,pearlite content,pearlite lamellar spacing and main composition are used for modeling by Gradient Tree Boosting and Gaussian Process algorithm.It significantly achieves the excellent performance of accuracy,with the mean relative errors less than 0.7%and the maximum relative errors less than 2.0%.This data-driven,globally-optimized and smart prediction method of tensile strength has advantages over traditional costly and time-consuming experimental trial,and can dramatically accelerate material design and process optimization of pearlitic steel,may also being applicable to other structural materials.The high accuracy of prediction ensures a reliable real-time forecast of tensile strength and provides a judgment basis for online quality assessment,which makes great contribution to lowering costs and improving efficiency of industrial production.