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 22 publications

(14 citation statements)

References 27 publications

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“…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%

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Machine learning for polymeric materials: an introduction

Cencer

Moore

Assary

2021

Polymer International

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Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure–property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis. © 2021 Society of Industrial Chemistry.

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Section: Approachesmentioning

confidence: 99%

Section: Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning for polymeric materials: an introduction

Cencer

Moore

Assary

2021

Polymer International

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Development of machine learning models for the prediction of erosion wear of hybrid composites

Mahapatra,

Satapathy

2024

Polymer Composites

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This article reports on development of an adaptive framework for predicting the erosion performance of polymer composites using certain statistical and machine learning (ML) models. For this, ramie‐epoxy composites reinforced with variations (0–30 wt%) of sponge iron slag (an iron industry waste) are considered. The composites are fabricated and then subjected to high temperature solid particle erosion wear trials following Taguchi's L27 orthogonal array. The effects of different control factors on the erosion rate in an interactive environment are appraised by analysis of variance (ANOVA) which reveals the filler content as the most significant factor contributing 66.21%, followed by impact velocity (22.86%) and impingement angle (2.28%). A regression model based on the input–output parameters obtained from experimentation is constructed for prediction of erosion rate. Further, four predictive models using different machine learning algorithms are also proposed to predict the erosion rate of the composites. The feasibility and performance of each ML model is assessed using appropriate performance metrics. Among all the models, the gradient boosting machine model is found to be the most reliable model exhibiting the highest prediction accuracy and least errors.Highlights Development of novel class of composites reinforced with sponge iron slag. Database creation based on erosion wear experimentation on the composites. Data‐driven modeling for prediction of erosion rates using machine learning. Comparison of performance of different models and identifying the best one.

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Prediction of Marshall stability of asphalt concrete reinforced with polypropylene fibre using different soft computing techniques

Jalota

Suthar

2023

Soft Comput

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

Cited by 22 publications

References 27 publications

Machine learning for polymeric materials: an introduction

Machine learning for polymeric materials: an introduction

Development of machine learning models for the prediction of erosion wear of hybrid composites

Prediction of Marshall stability of asphalt concrete reinforced with polypropylene fibre using different soft computing techniques

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