Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM

Abstract

Carbon fiber fabrics are important engineering materials. However, it is confusing to classify different carbon fiber fabrics, leading to risks in engineering processes. Here, a classification method for four types of carbon fiber fabrics is proposed using artificial neural networks (ANNs) and support vector machine (SVM) based on 229 experimental data groups. Sample width, breaking strength and breaking tenacity were set as independent variables. Quantified numbers for the four carbon fiber fabrics were set as dependent variables. Results show that a multilayer feed-forward neural network with 21 hidden nodes (MLFN-21) has the best performance for classification, with the lowest root mean square error (RMSE) in the testing set.

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

“…Machine learning has been applied to manufacturing to differentiate carbon fiber fabric type [71], diagnose locomotion gait faults for reconfigurable robots [72], machine tool health status [73], early stage electrical fault detection in induction motors [74], sealing surface defect for chili oil production line [75], metallic surfaces for a flat metal component production line [76], aerospace The generated hybrid fault model was installed onto the edge devices to provide fault prediction. Input gyroscope, accelerometer, temperature, humidity, ambient light, and air quality sensor data were then handled, processed, and analyzed in the edge device without requiring network communication with the cloud server, minimizing network costs, while simultaneously improving data processing and analysis speeds.…”

“…Machine learning has been applied to manufacturing to differentiate carbon fiber fabric type [71], diagnose locomotion gait faults for reconfigurable robots [72], machine tool health status [73], early stage electrical fault detection in induction motors [74], sealing surface defect for chili oil production line [75], metallic surfaces for a flat metal component production line [76], aerospace deburring prediction [77], remaining turbofan engine useful life [78], etc. The present study adopted a machine learning model in the edge devices to detect fault events during assembly line process.…”

Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles

“…However, this procedure is slow, and it depends on the observer's ability to identify species, which leads to bias [9]. Hence, machine learning techniques are being applied in many research areas to design automatic classification intelligent systems, such as mosquito identification based on morphological features [10], carbon fiber fabrics classification to minimize risks in engineering processes [11], automatic recognition of arrhythmias for the diagnosis of heart diseases [12], or this work where bioacustic signals of reptile species are used for taxonomic classification.…”

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks

“…Despite their tremendous features, ANNs have had a very limited application in the field of materials science [11,23,24]-let alone the prediction of IV curves. In [11], ANNs were used for the prediction of the current-voltage curves for a square array of nano-engineered periodic antidots.…”