Microstructure and Properties of Engineering Materials

Clemens, Helmut; Scheu, Christina

doi:10.1002/9783527621927.ch1

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Nondestructive testing methods are typically used to examine sample strength and its changes over time [2]. NDT methods cover a wide range of analytical techniques for damage-free material testing.…”

Section: Classification Of Nondestructive Testingmentioning

confidence: 99%

“…There are generally three types of bonds in engineered materials i.e., metallic, ionic, and covalent bonds. Metals and alloys are comprised of metallic bonds; semiconductors form covalent bonds, and ceramics have both ionic and covalent bonds [2].…”

Section: Introductionmentioning

confidence: 99%

“…In crystalline materials, the atoms are coordinated in repeating clusters known as a lattice structure. Although there are numerous potential crystal structures, some of the more commonly observed structures in metals are face-centered cubic (fcc), body-centered cubic (bcc), hexagonal closed-packed (hcp), and tetragonal [2]. Numerous metals and their alloys exist in more than one crystal structure depending on composition and temperature, but the majority fall within these four gem structures.…”

Section: Introductionmentioning

confidence: 99%

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Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

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2021

Sensors

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Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

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Section: Classification Of Nondestructive Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

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Skarka

Ontipuli³

2021

Sensors

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“…It is well known that the mechanical, electrical, magnetic, and optical properties of ceramics, metals, and their alloys can all be tailored and modified by changes in microstructures [ 25 ]. Consequently, grain size engineering is a common practice used to optimize the physical properties of a material without altering its chemistry [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Workflow for Autonomous Grain Size Analysis

2022

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Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn’s method) and planimetric (Saltykov’s method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method.

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“…A first one is the particle scale and corresponds to the identification of the different phases constituting particles in terms of chemical composition and crystal structure. Important properties of particles depend on the arrangements of atoms and types of bonding within each phase (Clemens et al, 2008). A second scale of observation is at the level of group of particles, or microstructure.…”

Section: Introductionmentioning

confidence: 99%