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

Abstract

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.

Keywords:

• Grain size
• Machine learning
• Machining
• Microhardness
• Titanium