Material Property Determination of Vibration Fatigued DMLS and Cold-Rolled Nickel Alloys

An experimental procedure for qualifying material properties from cyclically worked parts was investigated in support of aging gas turbine engines and digital twin initiatives. For aging components, remanufacturing or repair efforts are necessary to sustain the life cycles of engines; and for digital twin, the virtual representation of a part requires accurate geometric and component material property measurement. Therefore, having an effective, non-destructive way to assess the material performance of parts is necessary. Since low cycle, low strain, mechanical testing is the ideal experimental approach for non-destructively assessing material properties, investigating the accuracy and trends of tensile properties of fatigue loaded parts was important. The fatigued parts used for this study were specimens tested according to the George Fatigue Method, and the materials observed were cold-rolled Inconel Alloys 625 and 718, and direct metal laser sintering (DMLS) Nickel Alloy 718. The tensile material properties were compared against pristine (non-fatigued) and published data. The comparison for the cold-rolled 625 and 718 results show an increase and a decrease, depending on rolling direction, of tensile strength due to the effects of fatigue cycles; however, the variation of the vibration affected tensile properties are all within one standard deviation of the pristine data. The comparisons of DMLS Nickel Alloys was conducted against two sets of alloys from different suppliers, and the results showed that the tensile properties are sensitive to DMLS manufacturing parameters and post-sintering processes. A digital twin related, nondestructive, material property determination technique is also discussed in this manuscript. The true alloy density was determined with the water displacement method, and elastic modulus is determined with an iterative Ritz method model. The modulus is under-predicted with this method, but suggestions for improving the model are discussed.