3.13 Application of High Temperature Polymer Matrix Composites to Engine Intake Valves

Radford, Donald W.

doi:10.1016/b978-0-12-803581-8.10339-x

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…As discussed in Section 1, rapid metallographic approaches based on edge detection and image binarization have been used to study a wide variety of problems [2][3][4][5][6][7][8][12][13][14][15]. The creation of image-processing algorithms with various levels of sophistication and computational efficiency has been extensively addressed with the introduction of several novel FD methods [38][39][40][41][42][43].…”

Section: Integrated Image Processing Toolsetmentioning

confidence: 99%

“…Rapid developments in construction, future power generation, and transportation technology demand better materials to improve the integrity and safety of engineering systems [1]. In this regard, the metallography approach has helped researchers understand a material's behavior such as the composition, amorphization, work hardening, heat treatment, and fracture toughness [2][3][4][5][6] from a microstructural point of view [2,3]. Understanding these microstructural aspects of a material's behavior can be beneficial for preventing the material fracture caused by fatigue, creep, and an external force impact [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation

Dharmawan

Lee

2022

Applied Sciences

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Grain boundaries (GBs), which are among the mechanical properties of a material, are a microstructural aspect that contributes to the overall behavior of metal. A deep understanding of the behavior of the GBs’ deformation, dislocation, and fracture will encourage the rapid development of new materials and lead to the better operation and maintenance of materials during their designed lifetimes. In this study, an integrated image processing toolset is proposed to provide an expeditious approach to extracting GBs, tracking their location, and identifying their internal deformation. This toolset consists of three integrated algorithms: image stitching, grain matching, and boundary extraction. The algorithms are designed to simultaneously integrate high and low spatial resolution images for gathering high-precision boundary coordinates and effectively reconstructing a view of the entire material surface for the tracing of the grain location. This significantly reduces the time needed to acquire the dataset owing to the ability of the low spatial resolution lens to capture wider areas as the base image. The high spatial resolution lens compensates for any weakness of the base image by capturing views of specific sections, thereby increasing the observation flexibility. One application successfully described in this paper is tracking the direction of the metal grain deformation in global coordinates by stacking a specific grain before and after the deformation. This allows observers to calculate the direction of the grain deformation by comparing the overlapping areas after the material experiences a load. Ultimately, this toolset is expected to lead to further applications in terms of observing fascinating phenomena in materials science and engineering.

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