Yiğit M. Arısoy

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

Titanium and its alloys are today used in many industries including aerospace, automotive, and medical device and among those Ti–6Al–4 V alloy is the most suitable because of favorable properties such as high strength-to-weight ratio, toughness, superb corrosion resistance, and bio-compatibility. Machining induced surface integrity and microstructure alterations size play a critical role in product fatigue life and reliability. Cutting tool geometry, coating type, and cutting conditions can affect surface and subsurface hardness as well as grain size. In this paper, predictions of machining induced microhardness and grain size are performed by using 3D finite element (FE) simulations of machining and machine learning models. Microhardness and microstructure of machined surfaces of Ti–6Al–4 V are investigated. Hardness measurements are conducted at elevated temperatures to develop a predictive model by utilizing FE-based temperature fields for hardness profile. Measured hardness, grain size, and fractions are utilized in developing predictive models. Predicted microhardness profiles and grain sizes are then utilized in understanding the effect of machining parameters such as cutting speed, tool coating, and edge radius on the surface integrity. Optimization using genetic algorithms is performed to identify most favorable tool edge radius and cutting conditions.

Investigations on Machining Induced Microstructural Changes in Inconel 100

Inconel-100 (IN-100) which is a nickel-base alloy is used in metal components operate at the hot sections of gas turbines and jet engines due its excellent strength and corrosion resistance at high temperatures. This work presents investigations on the microstructural changes that occur during the machining process and specifically focuses on the effects of cutting parameters on the resultant average grain sizes of γ and γ′ phases and microhardness of the finished surfaces. Furthermore, finite element simulations have been performed to calculate the changes in grain sizes due to dynamic recrystallization by using the Johnson-Mehl-Avrami-Kolmogorov model.Copyright

A Sensitivity Analysis Study on the Material Properties and Process Parameters for Selective Laser Melting of Inconel 625

A prediction of the 2-D temperature profile and melt pool geometry for Selective Laser Melting (SLM) of Inconel 625 metal powder with a numerically-based approach for solving the heat conduction-diffusion equation was established in this paper. A finite element method solution of the governing equation was developed. A review of the current efforts in numerical modeling for laser-based additive manufacturing is presented. Initially, two-dimensional (2-D) temperature profiles along the scanning (x-direction) and hatch direction (y-direction) are calculated for a moving laser heat source to understand the temperature rise due to heating during SLM. The effects of varying laser power, scanning speed and the powder material’s density are analyzed. Based on the predicted temperature distributions, melt pool geometry, i.e. the locations at which melting of the powder material occurs, is determined. The results are chiefly compared against the published literature on melt pool data. The main goal of this research is to develop a computational tool with which investigation of the importance of various laser, material, and process parameters on the built dimensional quality in laser-based additive manufacturing becomes not only possible but also practical and reproducible.Copyright