Tungsten

Brown, T.J.; Pitfield, P.E.J.

doi:10.1002/9781118755341.ch16

Cited by 22 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strong interatomic bonding, which is responsible for a material's solid structure at high temperatures, can lead to high elastic constants as well. For instance, metals like tungsten and molybdenum, as well as ceramics such as alumina and silicon carbide, possess strong metallic, ionic, and covalent bonding, resulting in high melting points and elastic constants 52,53,54 .…”

Section: Task-specific Ranking Of Featuresmentioning

confidence: 99%

Machine Learning for Elastic Properties of Materials: A predictive benchmarking study in a domain-segmented feature Space

Banik

Balasubramanian

Manna

et al. 2023

Preprint

View full text Add to dashboard Cite

Insights into the unique characteristics across different classes of materials are crucial for Machine Learning (ML) tools and reveal the physics behind the studied process. Traditional predictive modeling of elastic properties of materials is limited to only a few classes of materials and a small set of ML tools despite the broad applications of these materials. While in recent years, Graph neural networks (GNNs) have outshined traditional ML models in terms of predictability, their intensive data requirement and lack of interpretability may limit practical applicability. In this work, we developed a domain-segmented feature space using a diverse set of material attributes and performed a predictive analysis of elastic properties of materials using state-of-the-art ML tools. By deducing the model-independent overall ranking of the features based on feature importance learned by each model, the knowledge is then transferred to GNNs. The findings indicate a saturation limit in the predictability of traditional ML models, but the transfer of task-specific feature importance knowledge to the GNNs can enhance their performance by reducing their data requirement while retaining considerable accuracy.

show abstract

Section: Task-specific Ranking Of Featuresmentioning

confidence: 99%

Machine Learning for Elastic Properties of Materials: A predictive benchmarking study in a domain-segmented feature Space

Banik

Balasubramanian

Manna

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Tungsten, also known as Wolfram (W), has a unique range of properties, such as high thermal and electrical conductivities, high density (19254 kg/m3 at 25°C) and high melt point (3422°C), which makes it an essential component for many applications [41]. The main tungsten ores are scheelite (CaWO 4 ) and wolframite (FeWO 4 ), corresponding to 30% and 70% of the total supply respectively [42].…”

Section: Less Common Residual Elements (Sb W Co Mo Ni)mentioning

confidence: 99%

“…The application of tungsten is divided by 4 different areas as shown in Figure 4. Hard metals (65%) consist of the formation of tungsten carbide (WC) and cemented carbides [41]. Tungsten carbide is largely employed in construction, petroleum, mining and metal-working industries, especially where they need to withstand higher temperatures [43].…”

Section: Less Common Residual Elements (Sb W Co Mo Ni)mentioning

confidence: 99%

Monitoring of less-common residual elements in scrap feeds for EAF steelmaking

Miranda

Assis

Brooks

et al. 2019

Ironmaking & Steelmaking

View full text Add to dashboard Cite

The steel scrap quality is classified according to the amount of residual elements. However, there is a limited analysis of some of the less common residual elements such as antimony (Sb), tungsten (W), cobalt (Co), nickel (Ni) and molybdenum (Mo) mixed with steel scrap received by steel plants. This paper reports a historical analysis of these elements in the steel compositions from two steel plants, located in different regions, during 2011 to 2018, analysing around 21000 heats. The concentration of W and Sb dropped by 0.002%, due to the increasing recovery of non-ferrous metals from mixed scrap. It was found that the calibration and maintenance of the spectrometer used to analysis had a direct impact on the results. The presence of Sb, W and Co in the steel indicates contamination from other materials, since 80% of the use of these elements is in applications associated with non-ferrous materials. This is the first analysis of these elements in steel scrap reported the literature and the conclusion of the study is that more attention should be paid to monitor less common residuals elements, because analysis of these elements can provide information about the quality of the metal scrap.

show abstract

“…Due to the advantages of high melting point, great corrosion resistance, superior electrical conductivity, and high-temperature strength [ 1 , 2 , 3 ], tungsten is widely used in integrated circuits, nuclear material, the military industry, medical equipment, and other fields [ 4 , 5 , 6 ]. The surface quality of the material has a significant influence on the service performance and service life of the workpiece.…”

Section: Introductionmentioning

confidence: 99%

Effects of pH Values and H2O2 Concentrations on the Chemical Enhanced Shear Dilatancy Polishing of Tungsten

Wang

Chen

et al. 2022

Micromachines

View full text Add to dashboard Cite

In order to obtain tungsten with great surface qualities and high polishing efficiency, a novel method of chemical enhanced shear dilatancy polishing (C-SDP) was proposed. The effects of pH values and H2O2 concentrations on the polishing performance of tungsten C-SDP were studied. In addition, the corrosion behaviors of tungsten in solutions with different pH values and H2O2 concentrations were analyzed by electrochemical experiments, and the valence states of elements on the tungsten surface were analyzed by XPS. The results showed that both pH values and H2O2 concentrations had significant effects on tungsten C-SDP. With the pH values increasing from 7 to 12, the MRR increased from 6.69 µm/h to 13.67 µm/h. The optimal surface quality was obtained at pH = 9, the surface roughness (Ra) reached 2.35 nm, and the corresponding MRR was 9.71 µm/h. The MRR increased from 9.71 µm/h to 34.95 µm/h with the H2O2 concentrations increasing from 0 to 2 vol.%. When the concentration of H2O2 was 1 vol.%, the Ra of tungsten reached the lowest value, which was 1.87 nm, and the MRR was 26.46 µm/h. This reveals that C-SDP technology is a novel ultra-precision machining method that can achieve great surface qualities and polishing efficiency of tungsten.

show abstract

Tungsten

Cited by 22 publications

References 33 publications

Machine Learning for Elastic Properties of Materials: A predictive benchmarking study in a domain-segmented feature Space

Machine Learning for Elastic Properties of Materials: A predictive benchmarking study in a domain-segmented feature Space

Monitoring of less-common residual elements in scrap feeds for EAF steelmaking

Effects of pH Values and H2O2 Concentrations on the Chemical Enhanced Shear Dilatancy Polishing of Tungsten

Contact Info

Product

Resources

About