TY - CONF A1 - Zia, Ghezal Ahmad Jan A1 - Torres, Benjami Moreno A1 - Skrotzki, Birgit A1 - Bayerlein, Bernd T1 - Quantitative Precipitate Analysis of an Age-Hardenable Aluminium Alloy Using a Deep Learning Approach N2 - Mechanical properties of metals and their alloys are strongly governed by their microstructure. The nanometer-sized precipitates in hardenable wrought aluminium alloys, which can be controlled by heat treatment, act as obstacles to dislocation movement within the material and are critical to the mechanical performance of the component, in this case a radial compressor wheel of a ships’ engine. TEM-based image analysis is essential for the study to investigate the microstructural changes (precipitation coarsening) that occur as a result of ageing at elevated temperatures. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Aluminium Alloy KW - Deep Learning KW - TEM Image PY - 2022 UR - https://dgm.de/mse/2022/ AN - OPUS4-55949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -