3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms

New developments at synchrotron beamlines and the ongoing upgrades of synchrotron facilities allow the possibility to study complex structures with a much better spatial and temporal resolution than ever before. However, the downside is that the data collected are also significantly larger (more than several terabytes) than ever before, and post-processing and analyzing these data is very challenging to perform manually. This issue can be solved by employing automated methods such as machine learning, which show significantly improved performance in data processing and image segmentation than manual methods. In this work, a 3D U-net deep convolutional neural network (DCNN) model with four layers and base-8 characteristic features has been developed to segment precipitates and porosities in synchrotron transmission X-ray micrograms. Transmission X-ray microscopy experiments were conducted on micropillars prepared from additively manufactured 316L steel to evaluate precipitate information. After training the 3D U-net DCNN model, it was used on unseen data and the prediction was compared with manual segmentation. A good agreement was found between both segmentations. An ablation study was performed and revealed that the proposed model showed better statistics than other models with lower numbers of layers and/or characteristic features. The proposed model is able to segment several hundreds of gigabytes of data in a few minutes and could be applied to other materials and tomography techniques. The code and the fitted weights are made available with this paper for any interested researcher to use for their needs (https://github.com/manasvupadhyay/erc-gamma-3D-DCNN)

A synchrotron transmission X-ray microscopy study on precipitate evolution during solid-state thermal cycling of a stainless steel

International audienceDuring additive manufacturing of stainless steels, sub-micron sized oxide (i.e., MnSiO3 , SiO2 , and CrMn2O4) and non-oxide (i.e., sulfide, in particular MnS, and possibly carbides, phosphides and nitrides) precipitates form during solidification. But do they evolve during the subsequent solid-state thermal cycling (SSTC) until the end of the printing process? A recent study on subjecting thin-film lamellae extracted from an additively manufactured stainless steel to heating-cooling treatments inside a transmission electron microscope (TEM) confirmed that precipitate composition can indeed evolve during SSTC. However, that study could not provide any conclusive evidence on precipitate volume fraction, density, and size evolution. In this work, we have quantified these changes using a novel experimental procedure combining (i) micropillar extraction from an additively manufactured stainless steel, (ii) subjecting them to different SSTC (including annealing) inside a TEM, (iii) performing synchrotron transmission X-ray microscopy to identify precipitates, and (iv) using a machine learning model to segment precipitates and quantify precipitate volume fraction, density, and size. Comparing these quantities before and after each SSTC/annealing sequence reveals that new oxides nucleated during rapid SSTC with high maximum temperature. However, during slow SSTC with high maximum temperature and annealing, precipitates dissolve because of oxygen evaporation during SSTC inside the TEM. A new empirical relationship correlating precipitate sizes and cooling rates is proposed. It is in good agreement with data collected from conventional casting, directed energy deposition, and powder bed fusion processes