Computer vision in microstructural analysis

The following is a laboratory experiment designed to be performed by advanced-high school and beginning-college students. It is hoped that this experiment will create an interest in and further understanding of materials science. The objective of this experiment is to demonstrate that the microstructure of engineered materials is affected by the processing conditions in manufacture, and that it is possible to characterize the microstructure using image analysis with a computer. The principle of computer vision will first be introduced followed by the description of the system developed at Texas A&M University. This in turn will be followed by the description of the experiment to obtain differences in microstructure and the characterization of the microstructure using computer vision

Microstructural analysis of solar cell welds

Parallel-gap resistance welding of silicon solar cells with copper interconnects results in complex microstructural variations that depend on the welding variables. At relatively low heat input solid-state welds are produced. At medium heat the Ag-Cu eutectic forms resulting in a braze joint. High heat produces a fusion weld with complete melting of the silver layer on the silicon solar cell. If the silicon is also melted, cracking occurs in the silicon cell below the weld nugget. These determinations were made using light microscopy, microprobe, and scanning electron microscopy analyses

Microstructural analysis of skeletal muscle force generation during aging.

Human aging results in a progressive decline in the active force generation capability of skeletal muscle. While many factors related to the changes of morphological and structural properties in muscle fibers and the extracellular matrix (ECM) have been considered as possible reasons for causing age-related force reduction, it is still not fully understood why the decrease in force generation under eccentric contraction (lengthening) is much less than that under concentric contraction (shortening). Biomechanically, it was observed that connective tissues (endomysium) stiffen as ages, and the volume ratio of connective tissues exhibits an age-related increase. However, limited skeletal muscle models take into account the microstructural characteristics as well as the volume fraction of tissue material. This study aims to provide a numerical investigation in which the muscle fibers and the ECM are explicitly represented to allow quantitative assessment of the age-related force reduction mechanism. To this end, a fiber-level honeycomb-like microstructure is constructed and modeled by a pixel-based Reproducing Kernel Particle Method (RKPM), which allows modeling of smooth transition in biomaterial properties across material interfaces. The numerical investigation reveals that the increased stiffness of the passive materials of muscle tissue reduces the force generation capability under concentric contraction while maintains the force generation capability under eccentric contraction. The proposed RKPM microscopic model provides effective means for the cellular-scale numerical investigation of skeletal muscle physiology. NOVELTY STATEMENT: A cellular-scale honeycomb-like microstructural muscle model constructed from a histological cross-sectional image of muscle is employed to study the causal relations between age-associated microstructural changes and age-related force loss using Reproducing Kernel Particle Method (RKPM). The employed RKPM offers an effective means for modeling biological materials based on pixel points in the medical images and allow modeling of smooth transition in the material properties across interfaces. The proposed microstructure-informed muscle model enables quantitative evaluation on how cellular-scale compositions contribute to muscle functionality and explain differences in age-related force changes during concentric, isometric and eccentric contractions

Microstructural analysis of phase separation in iron chalcogenide superconductors

The interplay between superconductivity, magnetism and crystal structure in iron-based superconductors is a topic of great interest amongst the condensed matter physics community as it is thought to be the key to understanding the mechanisms responsible for high temperature superconductivity. Alkali metal doped iron chalcogenide superconductors exhibit several unique characteristics which are not found in other iron-based superconducting materials such as antiferromagnetic ordering at room temperature, the presence of ordered iron vacancies and high resistivity normal state properties. Detailed microstructural analysis is essential in order to understand the origin of these unusual properties. Here we have used a range of complementary scanning electron microscope based techniques, including high-resolution electron backscatter di raction mapping, to assess local variations in composition and lattice parameter with high precision and sub-micron spatial resolution. Phase separation is observed in the Csx Fe2-ySe2 crystals, with the minor phase distributed in a plate-like morphology throughout the crystal. Our results are consistent with superconductivity occurring only in the minority phase.Comment: Accepted for publication in a special edition of Supercond. Sci. Techno

High-temperature deformation and microstructural analysis for Si3N4-Sc2O3

It was indicated that Si3N4 doped with Sc2O3 may exhibit high temperature mechanical properties superior to Si3N4 systems with various other oxide sintered additives. High temperature deformation of samples was studied by characterizing the microstructures before and after deformation. It was found that elements of the additive, such as Sc and O, exist in small amounts at very thin grain boundary layers and most of them stay in secondary phases at triple and multiple grain boundary junctions. These secondary phases are devitrified as crystalline Sc2Si2O7. Deformation of the samples was dominated by cavitational processes rather than movements of dislocations. Thus the excellent deformation resistance of the samples at high temperature can be attributed to the very small thickness of the grain boundary layers and the crystalline secondary phase

Microembossing of ultrafine grained Al: microstructural analysis and finite element modelling

Ultra fine grained (UFG) Al-1050 processed by equal channel angular pressing (ECAP) and UFG Al-Mg-Cu-Mn processed by high pressure torsion (HPT) were embossed at both room temperature and 300 °C, with the aim of producing micro-channels. The behaviour of Al alloys during the embossing process was analysed using finite element (FE) modelling. The cold embossing of both Al alloys is characterised by a partial pattern transfer, a large embossing force, channels with oblique sidewalls and a large failure rate of the mould. The hot embossing is characterised by straight channel sidewalls, fully transferred patterns and reduced loads which decrease the failure rate of the mould. Hot embossing of UFG Al-Mg-Cu-Mn produced by HPT shows a potential of fabrication of microelectromechanical system (MEMS) components with micro channels

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

Microstructural analysis and magnetic characterization of native and magnetically modified montmorillonite and vermiculite

Two clay minerals of the similar 2 : 1 layer structure and chemical composition, vermiculite and montmorillonite, were studied using a wide spectrum of experimental methods in their original states and the magnetically modified states after mixing with microwave-synthesized iron oxide particles. This magnetic modification led to different microstructural morphology influencing magnetic behaviour at room and more pronounced at low temperatures.Web of Scienceart. no. 373810