Machine learning for polymeric materials: an introduction
Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …
researchers are using data science and polymer informatics to design new materials and …
[HTML][HTML] Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Reinforced composite is a preferred choice of material for the design of industrial lightweight
structures. As of late, composite materials analysis and development utilizing machine …
structures. As of late, composite materials analysis and development utilizing machine …
Approximation of the discharge coefficient of differential pressure flowmeters using different soft computing strategies
Due to its importance in flow measurement and instrumentation, as well as its frequent
application in differential pressure flowmeters, orifice discharge coefficient (C d) needs to be …
application in differential pressure flowmeters, orifice discharge coefficient (C d) needs to be …
[HTML][HTML] Machine learning regression tools for erosion prediction of WC-10Co4Cr thermal spray coating
The prediction of erosion in WC-10Co4Cr thermal spray coating is predicted using
regression machine learning technique. A pot tester helped to examine the erosion rate of …
regression machine learning technique. A pot tester helped to examine the erosion rate of …
A study of added sic powder in kerosene for the blind square hole machining of cfrp using electrical discharge machining
Abstract Carbon Fiber Reinforced Polymers (CFRPs) have been applied potentially for
various application components owing to their lightweight and better mechanical properties …
various application components owing to their lightweight and better mechanical properties …
Neural computing and Taguchi's methodbased study on erosion of advanced Mo2C–WC10Co4Cr coating for the centrifugal pump
Nowadays, computational and computing tools are widely used in prediction applications.
Neural computing is a modern and emerging technique to predict data efficiently and …
Neural computing is a modern and emerging technique to predict data efficiently and …
Estimation of infiltration rate using data-driven models
The infiltration rate is one of the primary processes of the hydrological cycle. It is the property
of water by which it moves through the soil particles. Good knowledge of the infiltration rate …
of water by which it moves through the soil particles. Good knowledge of the infiltration rate …
Experimental investigation and prediction of ECDM parameters on fiber reinforced SiC composite using hybrid ERNN-based Sparrow Search Optimization
V Manoharan, S Tamilperuvalathan - Materials Today Communications, 2023 - Elsevier
Abstract The Silicon Carbide (SiC) fiber-reinforced SiC ceramic matrix composites have
proved their outstanding performance on high thermal applications such as gas turbine …
proved their outstanding performance on high thermal applications such as gas turbine …
Development of machine learning models for the prediction of erosion wear of hybrid composites
This article reports on development of an adaptive framework for predicting the erosion
performance of polymer composites using certain statistical and machine learning (ML) …
performance of polymer composites using certain statistical and machine learning (ML) …
Kernel-based framework for improved prediction of discharge coefficient in vertically supported cylindrical weirs
The present study represents the first use of kernel-based models to predict discharge
coefficient (Cd) for two distinct types of cylindrical weirs, featuring vertical support and a 30 …
coefficient (Cd) for two distinct types of cylindrical weirs, featuring vertical support and a 30 …