Determination of the best microstructure and titanium alloy powders properties using neural network

Abstract

Purpose: Create a software product using a probabilistic neural network (PNN) and database based on experimental research of titanium alloys to definition of the best microstructure and properties of aerospace components. Design/methodology/approach: The database creation process for artificial neural network training was preceded by the investigation of the granulometric composition of the titanium powder alloys, study of microstructure, phase composition and evaluation of micromechanical properties of these alloys by the method of material plasticity estimation in the conditions of hard pyramidal indenters application. A granulometric analysis was done using a special complex of materials science for the images analysis ImageJ. Metallographic investigations of the powder structure morphology were carried out on the scanning electron microscope EVO 40XVP. Specimens for micromechanical testing were obtained by overlaying the titanium alloy powders on the substrate made of the material close to chemical composition. Substrates were prepared by pressing the powder mixture under the load of 400 MPa and following sintering at 1300°C for 1 hour. Overlaying was performed by an electron gun ELA-6 (beam current – 16 mA). Findings: According to the modelling results, a threshold of the PNN accuracy was established to be over 80%. A high level of experimental data reproduction allows us a full or partial replacement of a number of experimental investigations by neural network modelling, noticeably decreasing, in this case, the cost of the material creation possessing the preset properties with preserved quality. It is expected that this software can be used for solving other problems in materials science too. Research limitations/implications: The accuracy of the PNN algorithm depends on the number of input parameters obtained experimentally and forms a database for the training of the system. For our case, the amount of input data is limited. Practical implications: Previously trained system based on the PNN algorithm will reduce the number of experiments that significantly reduce costs and time to study. Originality/value: A software product on the basis of the PNN network was developed. The training sample was built based on a series of laboratory studies granulometric composition of the titanium powder alloys, study of microstructure, phase composition and evaluation of micromechanical properties of powder materials. The proposed approach allows us to determine the best properties of the investigated material for the design of aerospace components.