Automated microstructural analysis of titanium alloys using digital image processing

Abstract

Abstract. Titanium is a material that exhibits many desirable properties including a very high strength to weight ratio and corrosive resistance. However, the specific properties of any components depend upon the microstructure of the material, which varies by the manufacturing process. This means it is often necessary to analyse the microstructure when designing new processes or performing quality assurance on manufactured parts. For Ti6Al4V, grain size analysis is typically performed manually by expert material scientists as the complicated microstructure of the material means that, to the authors knowledge, no existing software reliably identifies the grain boundaries. This manual process is time consuming and offers low repeatability due to human error and subjectivity. In this paper, we propose a new, automated method to segment microstructural images of a Ti6Al4V alloy into its constituent grains and produce measurements. The results of applying this technique are evaluated by comparing the measurements obtained by different analysis methods. By using measurements from a complete manual segmentation as a benchmark we explore the reliability of the current manual estimations of grain size and contrast this with improvements offered by our approach.

Intelligent machining methods for Ti6Al4V: A review

“…Also, machining induces microstructural changes in the alloy due to high temperature at the cutting zone. DIP was used by authors 42,43,104 to characterize the microstructure of raw materials and machined parts. For instance, Campbell et al 42,43 developed a routine to segment and measure a grains in grey level images of Ti6Al4V microstructures.…”

“…DIP was used by authors 42,43,104 to characterize the microstructure of raw materials and machined parts. For instance, Campbell et al 42,43 developed a routine to segment and measure a grains in grey level images of Ti6Al4V microstructures. They used a watershed algorithm before thresholding to improve segmentation accuracy.…”

“…35,39,40 The processing steps behind Ti6Al4V alloy fabrication will produce a microstructure, characterized by the grain size, the alpha-beta-phase volume fraction and the phase morphology. 35,38,41–43…”

“…42,43,104,110 This is relevant because, the thermo-mechanical properties and machinability of Ti6Al4V are related to the material microstructure. 38,41–44 The volume fraction of the α and β phase in Ti6Al4V changes the deformation mechanism of this alloy during the machining process. Also, machining induces microstructural changes in the alloy due to high temperature at the cutting zone.…”

Algorithm for Identifying Objects in The Relief Image Using Watershed Segmentation

“…Therefore, to improve the realibility of phase separation, Campbell et al assess each region from Watershed segmentation based on the percentage of its constituent pixels that are classified as each phase. Next each region were label as alpha or beta phase to obtain a robust phase separation [4]. Galibourg et al compared the effect of automatic segmentation using a Watershed-based method with semi-automatic segmentation on 52 teeth micro computed tomograph image for accuracy and reproducibility of 3D reconstruction.…”