Analysis of second phase particles in metals using deep learning: Segmentation of nanoscale dispersoids in 6xxx series aluminium alloys (Al-Mg-Si)

During the homogenization heat treatment of 6xxx series aluminum alloys, nanoscale precipitates—commonly named dispersoids—are formed that influence material properties during further processing by extrusion, forging, or rolling, as well as final product quality. Obtaining dispersoid size distributions is commonly accomplished by manually counting and measuring the diameter of the particles in metallographic sections investigated by means of electron microscopy. An automatization of this process, while desired, is difficult due to varying backgrounds, brightness and contrast levels, noise, dispersoid morphologies as well as scratches and interference from other types of intermetallic phases. In order to segment dispersoids in a wide range of 6xxx series aluminum alloys, a neural network is trained on the basis of electron micrographs of different alloy samples that include various possible separation artifacts and is compared to several benchmark models. The neural network evaluated in this work shows promising results, consistent over all analyzed samples, with a maximum error of roughly 20% while the benchmark models show errors of up to 85%.