Grain size determination and limits to Hall-Petch behavior in nanocrystalline NiAl powders

An end-to-end computer vision methodology for quantitative metallography

Metallography is crucial for a proper assessment of material properties. It mainly involves investigating the spatial distribution of grains and the occurrence and characteristics of inclusions or precipitates. This work presents a holistic few-shot artificial intelligence model for Quantitative Metallography, including Anomaly Detection, that automatically quantifies the degree of the anomaly of impurities in alloys. We suggest the following examination process: (1) deep semantic segmentation is performed on the inclusions (based on a suitable metallographic dataset of alloys and corresponding tags of inclusions), producing inclusions masks that are saved into a separated dataset. (2) Deep image inpainting is performed to fill the removed inclusions parts, resulting in 'clean' metallographic images, which contain the background of grains. (3) Grains' boundaries are marked using deep semantic segmentation (based on another metallographic dataset of alloys), producing boundaries that are ready for further inspection on the distribution of grains' size. (4) Deep anomaly detection and pattern recognition is performed on the inclusions masks to determine spatial, shape, and area anomaly detection of the inclusions. Finally, the end-to-end model recommends an expert on areas of interest for further examination. The physical result can re-tune the model according to the specific material at hand. Although the techniques presented here were developed for metallography analysis, most of them can be generalized to a broader set of microscopy problems that require automation. All source-codes as well as the datasets that were created for this work, are publicly available at https://github.com/Scientific-Computing-Lab-NRCN/MLography .

Modelling and Experimental Validation of the Porosity Effect on the Behaviour of Nano-Crystalline Materials

Nano-crystalline metals have attracted considerable attention over the past two decades due to their increased mechanical properties as compared to their microcrystalline counterparts. However, the behaviour of nano-crystalline metals is influenced by imperfections introduced during synthesis or heat treatment. These imperfections include pores, which are mostly located in the area of grain boundaries. To study the behaviour of multiphase nano-crystalline materials, a novel fully parametric algorithm was developed. The data required for implementing the developed numerical model were the volume fraction of the alloying elements and their basic properties as well as the density and the size of randomly distributed pores. To validate the developed algorithm, the alloy composition 75 wt% tungsten and 25 wt% copper was examined experimentally under compression tests. For the investigation, two batches of specimens were used; a batch having a coarse-grained microstructure with an average grain diameter of 150 nm and a nanocrystalline batch having a grain diameter of 100 nm, respectively. The porosity of both batches was derived to range between 9% and 10% based on X-ray diffraction analyses. The results of quasi-static compression testing revealed that the nanocrystalline W-Cu material exhibited brittle behaviour which was characterised by an elastic deformation that led to fracture without remarkable plasticity. A compressive strength of about 1100 MPa was derived which was more than double compared to conventional W-Cu samples. Finite element simulations of the behaviour of porous nano-crystalline materials were performed and compared with the respective experimental compression tests. The numerical model and experimental observations were in good agreement.

Numerical Computation of Material Properties of Nanocrystalline Materials Utilizing Three-Dimensional Voronoi Models

Nanocrystalline metals have been the cause of substantial intrigue over the past two decades due to their high strength, which is highly sensitive to their microstructure. The aim of the present project is to develop a finite element two-phase model that is able to predict the elastic moduli and the yield strength of nanostructured material as functions of their microstructure. The numerical methodology uses representative volume elements (RVEs) in which the material microstructure, i.e., the grains and grain boundaries, is presented utilizing the three-dimensional (3D) Voronoi algorithm. The implementation of the 3D Voronoi particles was performed on the nanostructure investigation of ultrafine materials by SEM and TEM. Proper material properties for the grain interiors (GI) and grain boundaries (GB) were computed using the Hall-Petch equation and a dislocation-based analytical approach, respectively. The numerical outcomes show that the Young’s Modulus of nanostructured copper increased by increasing the crystallite volume fraction, while the yield strength increased by decreasing the grain size. The numerical predictions were strongly confirmed in opposition to finite element outcomes, experimental results from the open literature, and predictions from the rule of mixtures and the Mori-Tanaka analytical models.

A new approach to grain boundary engineering for nanocrystalline materials

A new approach to grain boundary engineering (GBE) for high performance nanocrystalline materials, especially those produced by electrodeposition and sputtering, is discussed on the basis of some important findings from recently available results on GBE for nanocrystalline materials. In order to optimize their utility, the beneficial effects of grain boundary microstructures have been seriously considered according to the almost established approach to GBE. This approach has been increasingly recognized for the development of high performance nanocrystalline materials with an extremely high density of grain boundaries and triple junctions. The effectiveness of precisely controlled grain boundary microstructures (quantitatively characterized by the grain boundary character distribution (GBCD) and grain boundary connectivity associated with triple junctions) has been revealed for recent achievements in the enhancement of grain boundary strengthening, hardness, and the control of segregation-induced intergranular brittleness and intergranular fatigue fracture in electrodeposited nickel and nickel alloys with initial submicrometer-grained structure. A new approach to GBE based on fractal analysis of grain boundary connectivity is proposed to produce high performance nanocrystalline or submicrometer-grained materials with desirable mechanical properties such as enhanced fracture resistance. Finally, the potential power of GBE is demonstrated for high performance functional materials like gold thin films through precise control of electrical resistance based on the fractal analysis of the grain boundary microstructure.

Increasing the strength of nanocrystalline steels by annealing: Is segregation necessary?

Hardening phenomena in nanocrystalline metals after annealing have been widely reported, and the subject of much recent debate. Solute segregation to grain boundaries and dislocation source hardening have been proposed to cause the strengthening. To shed light on the dominant mechanisms, we present results from mechanical experiments and atom probe tomography on samples with similar grain size but different amounts of solute segregation and different boundary chemistries.

HighP-TNano-Mechanics of Polycrystalline Nickel

We have conducted highP-Tsynchrotron X-ray and time-of-flight neutron diffraction experiments as well as indentation measurements to study equation of state, constitutive properties, and hardness of nanocrystalline and bulk nickel. Our lattice volume-pressure data present a clear evidence of elastic softening in nanocrystalline Ni as compared with the bulk nickel. We show that the enhanced overall compressibility of nanocrystalline Ni is a consequence of the higher compressibility of the surface shell of Ni nanocrystals, which supports the results of molecular dynamics simulation and a generalized model of a nanocrystal with expanded surface layer. The analytical methods we developed based on the peak-profile of diffraction data allow us to identify "micro/local" yield due to high stress concentration at the grain-to-grain contacts and "macro/bulk" yield due to deviatoric stress over the entire sample. The graphic approach of our strain/stress analyses can also reveal the corresponding yield strength, grain crushing/growth, work hardening/softening, and thermal relaxation under highP-Tconditions, as well as the intrinsic residual/surface strains in the polycrystalline bulks. From micro-indentation measurements, we found that a low-temperature annealing (T < 0.4 Tm) hardens nanocrystalline Ni, leading to an inverse Hall-Petch relationship. We explain this abnormal Hall-Petch effect in terms of impurity segregation to the grain boundaries of the nanocrystalline Ni.