Assessing Cast Aluminum Alloys With Computed Tomography Defect Metrics: a Gurson Porous Plasticity Approach

Abstract

Aluminum alloys have inherent tendencies to produce casting defects caused by alloying or metal melt flow inside the mold. The traditional detection method for these defects includes reduced pressure tests, which assess metal quality in a destructive manner. This leaves a gap between metal quality assessments and tensile test correlations. Computed tomography (CT) scans offer crucial assistance in evaluating the internal quality of castings without damaging the structure. This provides a valuable opportunity to couple mechanical tests with numerical methods such as finite element analysis to predict the mechanical performance of the alloy. The present study aims to evaluate the internal quality of cast aluminum alloys using CT scans and to correlate the defect metrics obtained from CT scans with mechanical test results. The Gurson-type material model and finite element methodology have been used to validate the correlation studies. Therefore, we propose a more holistic approach to predicting the behavior of metals by coupling damage models with CT scans and mechanical tests. The study investigates several CT metrics such as the defect volume, total defect surface, biggest defect surface, and projected area of defects. The conclusion reveals that CT scans provide crucial assistance in evaluating the internal quality of castings, and CT defect metrics can be used to build correlations between mechanical tests and CT evaluations. The study also suggests that the concept of adjusted representative material yield parameter (ARMY) or computed representative material yield parameter (CRMY) can be used to correlate CT metrics with mechanical strength in cast materials and parts for a given aluminum alloy. Overall, the study proposes a more comprehensive methodology to assess the quality of cast aluminum alloys and couple the quality to mechanical performance.