Materials Characterization and Diagnosis Using Variable Frequency Microwaves

MRS Proceedings ◽

10.1557/proc-430-73 ◽

1996 ◽

Vol 430 ◽

Cited By ~ 1

Author(s):

J. Billy Wei ◽

Zak Fathi ◽

Denise A. Tucker ◽

Michael L. Hampton ◽

Richard S. Garard ◽

...

Keyword(s):

Resonant Mode ◽

Diglycidyl Ether ◽

Materials Characterization ◽

Variable Frequency ◽

Product Quality Control ◽

Microwave Curing ◽

Microwave Assisted ◽

Characterization Of Materials ◽

Crucial Part

AbstractProduct quality control is a crucial part of manufacturing and usually involves materials characterization and diagnosis. Though various microwave assisted nondestructive evaluation (MA-NDE) systems have been fabricated for materials inspection, none of the systems can be applied to materials within a mold or reactor. A broadband variable frequency microwave based, resonant mode MA-NDE was studied as an alternative for characterization of materials within a cavity. The main advantage of the resonant mode MA-NDE are non-intrusive and volumetric diagnosis of the material inside a mold. The principles and possible applications of the resonant mode MA-NDE are discussed. Resonant mode MA-NDE was fully demonstrated by using Vani-Wave to trace material status during microwave curing of Diglycidyl Ether of Bisphenol A (DGEBA)/Diaminodiphenylsulphone (DDS) epoxy samples.

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Learning Representations of Inorganic Materials from Generative Adversarial Networks

Symmetry ◽

10.3390/sym12111889 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1889

Author(s):

Tiantian Hu ◽

Hui Song ◽

Tao Jiang ◽

Shaobo Li

Keyword(s):

Large Scale ◽

Inorganic Materials ◽

Molecular Composition ◽

Materials Characterization ◽

Generative Adversarial Networks ◽

Feature Engineering ◽

Material Research ◽

Generative Adversarial Network ◽

Characterization Of Materials

The two most important aspects of material research using deep learning (DL) or machine learning (ML) are the characteristics of materials data and learning algorithms, where the proper characterization of materials data is essential for generating accurate models. At present, the characterization of materials based on the molecular composition includes some methods based on feature engineering, such as Magpie and One-hot. Although these characterization methods have achieved significant results in materials research, these methods based on feature engineering cannot guarantee the integrity of materials characterization. One possible approach is to learn the materials characterization via neural networks using the chemical knowledge and implicit composition rules shown in large-scale known materials. This article chooses an adversarial method to learn the composition of atoms using the Generative Adversarial Network (GAN), which makes sense for data symmetry. The total loss value of the discriminator on the test set is reduced from 4.1e13 to 0.3194, indicating that the designed GAN network can well capture the combination of atoms in real materials. We then use the trained discriminator weights for material characterization and predict bandgap, formation energy, critical temperature (Tc) of superconductors on the Open Quantum Materials Database (OQMD), Materials Project (MP), and SuperCond datasets. Experiments show that when using the same predictive model, our proposed method performs better than One-hot and Magpie. This article provides an effective method for characterizing materials based on molecular composition in addition to Magpie, One-hot, etc. In addition, the generator learned in this study generates hypothetical materials with the same distribution as known materials, and these hypotheses can be used as a source for new material discovery.

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Guided Waves in Plates and Their Use in Materials Characterization

Applied Mechanics Reviews ◽

10.1115/1.3101707 ◽

1997 ◽

Vol 50 (5) ◽

pp. 247-284 ◽

Cited By ~ 235

Author(s):

D. E. Chimenti

Keyword(s):

Guided Waves ◽

Large Body ◽

Review Article ◽

Guided Wave ◽

Materials Characterization ◽

Current State ◽

Ultrasonic Characterization ◽

Characterization Of Materials ◽

Wave Methods

In this review article, the ultrasonic characterization of materials using guided plate waves and their usage to elucidate mechanical properties of plate-like structures is reviewed. The purpose here is to summarize and explain the large body of theoretical and experimental work in this developing field. It is also to gain a perspective on recent salient contributions and to analyze the current state of knowledge and practice in guided wave ultrasonics. Models of waves in plates are examined, as are the means to generate and detect them. Their application to several problems of current interest in materials characterization is treated in detail. In particular, composite materials and their inspection and characterization have been a major impetus in the development of guided wave methods. Techniques to inspect composites sensitively and reliably for defects and to probe their micromechanical behavior are a major focus of this article. Also considered are the characterization of adhesive bonds, the measurement of stress and texture, and the detection of defects using guided waves. This review article contains 362 references.

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The Role of Structure-to-Property-Relationships in Materials Characterization

MRS Proceedings ◽

10.1557/proc-591-119 ◽

1999 ◽

Vol 591 ◽

Author(s):

W. Morgner

Keyword(s):

Cast Iron ◽

Material Characterization ◽

Ball Bearing ◽

Bearing Steel ◽

Materials Characterization ◽

Characterization Of Materials ◽

Nondestructive Characterization ◽

Ball Bearing Steel

ABSTRACTThe paper deals with questions concerning the material characterization for steels in the field of engineering and metallurgy. Based on the structure-to-property-relationships, a procedure is proposed to strengthen the systematical development of methods for nondestructive characterization of materials. The state of the nondestructive characterization of metals is reviewed and applications are described in which adequate macroscopic physical properties are measured in order to characterize the materials state and properties nondestructively. The materials characterization of ball bearing steel and cast iron using multiparametrical approaches is discussed in detail.

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Micro-Raman Spectroscopy for the Characterization of Materials in Electronic and Photonic Devices

Microscopy and Microanalysis ◽

10.1017/s1431927600011107 ◽

1997 ◽

Vol 3 (S2) ◽

pp. 843-844

Author(s):

David D.Tuschel

Keyword(s):

Raman Spectroscopy ◽

Photonic Devices ◽

Materials Characterization ◽

Selection Rules ◽

Micro Raman Spectroscopy ◽

Optical And Electronic Properties ◽

Oriented Polycrystalline ◽

Characterization Of Materials ◽

Non Destructive

Materials characterization is the primary application of macro- and micro-Raman spectroscopy in our laboratory. Specifically, we wish to correlate chemical bonding and short to long range translational symmetry (including amorphous, highly oriented, polycrystalline, and single crystal materials) to physical, optical and electronic properties of materials and devices. Raman spectroscopy is particularly useful in this capacity because of its origin in the vibrational motions of chemically bonded atoms and its dependence upon crystal symmetry through the polarization selection rules. Furthermore, the high spatial resolution and non-destructive nature of micro-Raman spectroscopy make it ideal for in situcharacterization of electronic and photonic devices. We will present results of materials characterization studies, performed using macro- and micro-Raman spectroscopy, of electronic and photonic devices. In addition, we will discuss how the Raman polarization selection rules can be advantageously applied to device characterization.A primary area of investigation involves the study of ion-implanted and annealed Si by Raman spectroscopy.

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Some applications of electron beam microanalysis techniques in VLSI process evaluation

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010007463x ◽

1983 ◽

Vol 41 ◽

pp. 160-161

Author(s):

Simon Thomas

Keyword(s):

Integrated Circuits ◽

Process Evaluation ◽

Large Scale ◽

Technology Development ◽

Analytical Techniques ◽

Successful Implementation ◽

Analysis Of Failures ◽

Characterization Of Materials ◽

Circuits Vlsi

Trends in the technology development of very large scale integrated circuits (VLSI) have been in the direction of higher density of components with smaller dimensions. The scaling down of device dimensions has been not only laterally but also in depth. Such efforts in miniaturization bring with them new developments in materials and processing. Successful implementation of these efforts is, to a large extent, dependent on the proper understanding of the material properties, process technologies and reliability issues, through adequate analytical studies. The analytical instrumentation technology has, fortunately, kept pace with the basic requirements of devices with lateral dimensions in the micron/ submicron range and depths of the order of nonometers. Often, newer analytical techniques have emerged or the more conventional techniques have been adapted to meet the more stringent requirements. As such, a variety of analytical techniques are available today to aid an analyst in the efforts of VLSI process evaluation. Generally such analytical efforts are divided into the characterization of materials, evaluation of processing steps and the analysis of failures.

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Applications of ultramicrotomy for materials characterization

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100147004 ◽

1993 ◽

Vol 51 ◽

pp. 232-233

Author(s):

R.T. Blackham ◽

J.J. Haugh ◽

C.W. Hughes ◽

M.G. Burke

Keyword(s):

Materials Science ◽

Microstructural Analysis ◽

Analytical Electron Microscopy ◽

Austenitic Stainless Steels ◽

Specimen Preparation ◽

Preparation Technique ◽

Thermally Induced ◽

Radiation Induced ◽

Characterization Of Materials

Essential to the characterization of materials using analytical electron microscopy (AEM) techniques is the specimen itself. Without suitable samples, detailed microstructural analysis is not possible. Ultramicrotomy, or diamond knife sectioning, is a well-known mechanical specimen preparation technique which has been gaining attention in the materials science area. Malis and co-workers and Glanvill have demonstrated the usefulness and applicability of this technique to the study of a wide variety of materials including Al alloys, composites, and semiconductors. Ultramicrotomed specimens have uniform thickness with relatively large electron-transparent areas which are suitable for AEM anaysis.Interface Analysis in Type 316 Austenitic Stainless Steel: STEM-EDS microanalysis of grain boundaries in austenitic stainless steels provides important information concerning the development of Cr-depleted zones which accompany M23C6 precipitation, and documentation of radiation induced segregation (RIS). Conventional methods of TEM sample preparation are suitable for the evaluation of thermally induced segregation, but neutron irradiated samples present a variety of problems in both the preparation and in the AEM analysis, in addition to the handling hazard.

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