Convolutional neural networks (CNNs) were used for the semantic segmentation of angular
monocrystalline WC from NiCrBSi-WC optical microscopy images. This deep learning …

Hierarchical multiscale modeling of heterogeneous materials has traditionally relied upon a
deterministic estimation of constitutive properties when making microstructure-sensitive …

This paper demonstrates the feasibility of extracting quantitative linkages between optical
micrographs and mechanical properties of cold-rolled HSLA (high-strength low alloy) steels …

A new framework for the assessment of model probabilities of the different crystal plasticity
models for lamellar grains in Titanium alloys - IOPscience Skip to content IOP Science home …

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 …

Accelerating design and development of new materials by establishing process-structure-
property (PSP) linkages is one of the core contents of materials science. One of the …

This paper presents new digital image analysis protocols and workflows for the
quantification of size distributions of key microstructural features (eg, grain size, γ'size) and …

In this study, we address a practical issue encountered in the identification of in situ steel
microstructures using convolutional neural networks (CNNs) and propose a possible …

Discrete dislocation structures and their evolution are known to control the mechanical
properties of metal samples. However, the lack of computationally efficient and statistically …

In recent work, our research group has developed and demonstrated novel multi-resolution
protocols capable of extracting indentation stress-strain (ISS) curves from tests on individual …