X-ray diffraction-based estimation of remaining fatigue life in AA6061-T6 for “additive remanufacturing®”
DOI:
https://doi.org/10.15353/hi-am.v1i1.6802Keywords:
Additive remanufacturing, Cold spray, Fatigue damage assessment, Sustainability, XRDAbstract
Cold spray is an advanced additive manufacturing technology that is capable of restoration of damaged metallic components without exposing them to high temperatures. To expand the use of cold spray from restoring the geometry and structure of defective parts to full remanufacturing, extending their lifespan beyond the original life cycle by replacing internally damaged areas (typically only 10–15% of the part’s volume), the first step is to accurately assess the damage at the part’s hot spot. This study explores the capabilities of X-ray diffraction (XRD) as a non-destructive testing method for assessing damage in AA6061-T6. A set of dog-bone samples was prepared to introduce controlled damage at different levels. X-ray diffraction measurements were conducted on these samples to generate test data, to assess dislocation densities. These values offer a quantified measure of internal damage and provide insight into the microstructural evolution under fatigue loading. By using this method, this study aims to develop a reliable method for pre-additive remanufacturing® damage assessment. In corroboration with earlier studies, we show that XRD can effectively detect internal material damage using dislocation densities through XRD-measured parameters such as full width at half maxima (FWHM), a measure of XRD peak broadening used for analyzing dislocation and strain. Integrating XRD-based damage assessment with cold spray additive manufacturing can enable precise and localized repairs. By implementing cold spray remanufacturing, this method can significantly reduce material waste, a major contributor to the greenhouse gas emissions, and extend components' lifespans across various industries, promoting sustainability and circular economy.
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Copyright (c) 2025 Sepehr Ghazimorady, Sina Negarandeh, Yi Chen, Melika Morafegh, Verena Kantere, Hamid Jahed

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.