Machine Learning-Based Erosion Behavior of Silicon Carbide Reinforced Polymer Composites

Kharb, Sandeep Singh; Antil, Parvesh; Singh, Sarbjit; Antil, Sundeep Kumar; Sihag, Parveen; Kumar, Anil

doi:10.1007/s12633-020-00497-z

Cited by 25 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…25 Regression models can predict refractive indices of linear polymers 92 and erosion behavior of silicon carbide-reinforced polymer composites. 4 Supervised learning is most effective and useful when there is a large accurate dataset available for training, and directly measuring the predicted property is an intensive process. Ideally, all models of this type would be shared in a format that makes them useable to researchers without having to recreate the training process.…”

Section: Approachesmentioning

confidence: 99%

“…Supervised learning with a deep neural network has been used to predict solvents and non‐solvents for a polymer 91 and polymer phase transitions 25 . Regression models can predict refractive indices of linear polymers 92 and erosion behavior of silicon carbide‐reinforced polymer composites 4 . Supervised learning is most effective and useful when there is a large accurate dataset available for training, and directly measuring the predicted property is an intensive process.…”

Section: Approachesmentioning

confidence: 99%

“…For example, the monomer(s) structure, synthesis method and processing control the chemical structure, morphology and hence properties of the final polymer 2 . Additionally, researchers are increasingly considering sustainability of monomer sourcing, interactions between a polymer and its environment, polymer aging behavior and end of life (whether as waste, or recyclable) 3,4 . These relationships are schematically shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Polymer informatics has been applied to essentially every aspect of the polymer lifecycle. It has been used to design new monomers for various applications 12,15,16 ; engineer reactions 17 ; model processing conditions and parameters 18–20 ; identify and predict polymer conformations and phases 21–26 ; predict materials properties 27–35 ; and finally offer insight into wear and end of life 4,36–39 . Most polymer informatics literature focuses on property prediction, but recently other aspects of polymer synthesis, processing and lifetime have been gaining attention 14,17,40 .…”

Section: Introductionmentioning

confidence: 99%

“…2 Additionally, researchers are increasingly considering sustainability of monomer sourcing, interactions between a polymer and its environment, polymer aging behavior and end of life (whether as waste, or recyclable). 3,4 These relationships are schematically shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine learning for polymeric materials: an introduction

Cencer

Moore

Assary

2021

Polymer International

Get access via publisher Add to dashboard Cite

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

show abstract

Section: Approachesmentioning

confidence: 99%