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.

The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

“…For modeling of the proposed methods, it was used the DataSet from [8,9]. Based on the studies from [8,25], it was possible to distinguish four groups of characteristics that effect a composition of an alloy, which can be synthesized from four different titanium alloys powder components (Fig. 1).…”

“…1), which are characteristic of this task, sometimes lead to an ineffective solution of the task, in particular by tools of computing intelligence [19]. In [8], the solution accuracy of the classification task (on the same data as in the work) using the Probabilistic Neural Network was 70%.…”

“…The problem of developing methods for the effective classification of titanium alloy powders is described in [8,9]. In particular, in [8], a computational intelligence method for determining the optimal microstructure and titanium alloy powders properties is developed. The tool used for this is the Probabilistic Neural Network.…”

Development of machine learning method of titanium alloy properties identification in additive technologies

“…of artificial intelligence [9,10] increases the efficiency of the procedure for developing or designing new materials. Traditional approaches allow obtaining all necessary information about the material properties [7], and the usage of powerful [11], modern ML algorithms [12][13] makes this process easier, shorter and cheaper.…”

“…Artificial intelligence tools can be used to reduce the duration, as well the cost of investigation of the properties of spherical and nonspherical titanium alloy powders [10]. The conducted literature review has shown the feasibility of using the algorithms of machine learning to solve this task, in particular on the basis of the Random Forest algorithm and the Kolmogorov-Gabor polynomial.…”