Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

3D FEM simulation of helical milling hole process for titanium alloy Ti-6Al-4V

As an emerging hole-machining methodology, helical milling process has become increasingly popular in aeromaterials manufacturing research, especially in areas of aircraft structural parts, dies, and molds manufacturing. Helical milling process is highly demanding due to its complex tool geometry and the progressive material failure on the workpiece. This paper outlines the development of a 3D finite element model for helical milling hole of titanium alloy Ti-6Al-4V using commercial FE code ABAQUS/Explicit. The proposed model simulates the helical milling hole process by taking into account the damage initiation and evolution in the workpiece material. A contact model at the interface between end-mill bit and workpiece has been established and the process parameters specified. Furthermore, a simulation procedure is proposed to simulate different cutting processes with the same failure parameters. With this finite element model, a series of FEAs for machined titanium alloy have been carried out and results compared with laboratory experimental data. The effects of machining parameters on helical milling have been elucidated, and the capability and advantage of FE simulation on helical milling process have been well presented.