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Cluster identification in AA5754 aluminium sheets using mathematical morphology analysis
Authors:A. TEWARI,S. TIWARI,P. BISWAS,&   R. K. MISHRA&dagger  
Affiliation:India Science Lab, General Motors R&D Centre India, Bangalore, India;Materials &Processes Lab, General Motors R&D Centre, Warren, MI, U.S.A.
Abstract:Quantitative image analysis of particle distribution in the microstructure of continuous cast (CC) and direct chill cast (DC) AA5754 aluminium alloy sheets have been conducted. This information can be used as an input for modelling mechanical deformation and instability in these materials. The quantitative analysis reveals that there are significant differences in the microstructure of the two materials even though the total content of second‐phase particles is statistically similar. Qualitative observation shows the second‐phase particles to be arranged in the form of streaks parallel to the rolling direction in the CC sheets and in a uniform random manner in the DC sheets. The main difference in the geometric microstructure of the CC and DC material is the spatial arrangement of the second‐phase particles. A new mathematical technique called proximity analysis is developed to identify clusters and group of particles belonging to a cluster. Quantification through proximity analysis reveals that the particle clusters in CC sheet are in the form of long clusters (streaks) parallel to the rolling direction and are significantly longer than those in DC sheets (with the largest cluster in CC being four times larger than DC), and also have anisotropic angular orientation parallel to the rolling direction. The lower value of fracture strain observed in the CC sheets compared to DC sheets is attributed to a combination of large sizes of clusters and their preferential alignment along the rolling direction in the CC microstructure.
Keywords:AA5754 aluminium alloy    cluster identification    continuous casting    direct chill casting    mathematical morphology
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