首页|Studies from Department of Mechanical Engineering Update Current Data on Machine Learning (A Study on Prediction of Friction Characteristics from Speckle Patter ns of Friction Surfaces Using Machine Learning)
Studies from Department of Mechanical Engineering Update Current Data on Machine Learning (A Study on Prediction of Friction Characteristics from Speckle Patter ns of Friction Surfaces Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from the Department of Mech anical Engineering by NewsRx correspondents, research stated, “The accurate pred iction of friction coefficients is crucial for the maintenance of sliding mechan ical components to enable the timely detection of potential failures.” Our news editors obtained a quote from the research from Department of Mechanica l Engineering: “Traditional methods rely on sensors like load cells and strain g auges to measure friction coefficients. However, these conventional techniques f ace challenges in real-time measurement during machine operation owing to physic al constraints associated with sensor placement. To address this limitation, thi s study investigates the application of laser speckle patterns for predicting fr iction coefficients through a novel approach using convolutional neural networks (CNNs). The laser speckle technique offers rich surface condition data, while C NNs, which are particularly advanced in managing vast datasets, excel in establi shing relationships between diverse factors for precise inference, classificatio n, and prediction. Utilizing ResNet, a leading CNN architecture, a new friction tester capable of concurrently recording friction coefficients and speckle patte rns in a cylinder-on-disk friction test was developed. The findings reveal that the CNN-based method, especially with ResNet, attained a coefficient of determin ation (R2) of 0.758, demonstrating its effectiveness in the accurate prediction of friction coefficients.”
Department of Mechanical EngineeringCy borgsEmerging TechnologiesMachine Learning
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