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

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.

Ferroaluminum deoxidation of ball-bearing steel during vacuum treatment

Metallurgist ◽  
1965 ◽  
Vol 9 (12) ◽  
pp. 746-747
Author(s):  
O. Kh. Fatkullin ◽  
V. I. Chukhlov ◽  
G. N. Oiks ◽  
I. I. Ansheles ◽  
S. S. Sivkov ◽  
...  
Keyword(s):  
Ball Bearing ◽  
Vacuum Treatment ◽  
Bearing Steel ◽  
Ball Bearing Steel

Wear and failure modes in the bandsawing operation when cutting ball-bearing steel

Wear ◽  
2005 ◽  
Vol 259 (7-12) ◽  
pp. 1144-1150 ◽  
Author(s):  
Mohammed Sarwar ◽  
Martin Persson ◽  
Håkan Hellbergh
Keyword(s):  
Failure Modes ◽  
Ball Bearing ◽  
Bearing Steel ◽  
Ball Bearing Steel

scholarly journals Learning Representations of Inorganic Materials from Generative Adversarial Networks

Symmetry ◽  
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.

scholarly journals Applied Sciences Special Issue: Ultrasonic Guided Waves

2019 ◽  
Vol 9 (18) ◽  
pp. 3869 ◽  
Author(s):  
Clifford J. Lissenden
Keyword(s):  
Applied Sciences ◽  
Scientific Inquiry ◽  
Guided Waves ◽  
Ultrasonic Guided Waves ◽  
Special Issue ◽  
Practical Applications ◽  
Material State ◽  
Characterization Of Materials ◽  
Nondestructive Characterization

The propagation of ultrasonic guided waves in solids is an important area of scientific inquiry due primarily to their practical applications for the nondestructive characterization of materials, such as nondestructive inspection, quality assurance testing, structural health monitoring, and for achieving material state awareness [...]

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