From Event: SPIE Commercial + Scientific Sensing and Imaging, 2018
One of the most important advantages of using high-diffusivity alloys like aluminium, in industry, is to reduce the weight without renouncing to high strength components. To accelerate the time of the mechanical characterisation, frequently experimental methods based on temperature measurements are adopted, even if in this case, these methods could involve in wrong estimations. In particular, the study of energy dissipations could produce some assessment errors of fatigue limit due to the fact that the fraction of the detected energy dissipated could be lower if compared to the effective energy intrinsically dissipated in the material due to damage. Furthermore, the fatigue life assessment of Aluminium alloys is problematic due to a non-distinct ‘knee’ in the S-N curve. To take into account these issues and to estimate the fatigue strength in rapid and accurate way, in this work, a method providing a specific thermal signal analysis is presented applied to an aluminium alloy 5754 H-111. Firstly, the well-known methods based on direct temperature measurements for estimating fatigue strength of metals, were applied on an aluminium alloy 5754 H111 in order to demonstrate their problematic application for high-diffusivity materials. Furtherly, a specific thermal signal analysis was adopted for extracting first and second order temperature variations as better parameter for fatigue strength assessment. This work questions the use of direct temperature evaluation in high diffusivity materials and fully replaces it in favor of an approach based on in-depth analysis of thermal signal by using thermoelastic and dissipative temperature variations.
Rosa De Finis, Davide Palumbo, Livia Maria Serio, and Luigi Alberto Ciro De Filippis, "Potentialities of thermal signal analysis approach for a rapid mechanical characterisation of high diffusivity materials," Proc. SPIE 10661, Thermosense: Thermal Infrared Applications XL, 106610J (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 17, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2304983.
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