Abstract
This study focuses on an evaluation of the significance of the fatigue-life reduction due to macro-porosity present in pressure-die-casted aluminium specimens. Three statistical models, i.e., univariate analysis of variance, multivariate analysis of variance and linear regression with dummy variables, were applied to test the statistical significance of the fatigue-life reduction. The three statistical models were applied for the case of experimentally determined fatigue-life data for an AlSi9Cu3 alloy with different levels of macro-porosity. Cylindrical specimens according to ASTM E606 were manufactured by pressure die casting using different manufacturing parameters (die pressure, die temperature) to artificially introduce detectable macro-pores into the specimens. The manufactured specimens were classified into three groups, representing their levels of porosity, which were identified based on x-ray images of the specimens. For each group, strain-controlled fatigue tests were performed at different strain levels. Of these approaches, linear regression with dummy variables proved to be the most appropriate, due to its ability to robustly identify the differences between the fatigue lives for different porosity levels.
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