Deep Learning and Bayesian Hyperparameter Optimization: A Data-Driven Approach for Diamond Grit Segmentation toward Grinding Wheel Characterization

Deep Learning and Bayesian Hyperparameter Optimization: A Data-Driven Approach for Diamond Grit Segmentation toward Grinding Wheel Characterization

Diamond grinding wheels (DGWs) have a central role in cutting-edge industries such as aeronautics or defense and spatial applications. Characterizations of DGWs are essential to optimize the design and machining performance of such cutting tools. Thus, the critical issue of DGW characterization lies...

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Bibliographic Details
Main Authors: Damien Sicard, Pascal Briois, Alain Billard, Jérôme Thevenot, Eric Boichut, Julien Chapellier, Frédéric Bernard
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Applied Sciences
Subjects:
deep learning
Bayesian hyperparameter optimization
computer vision
semantic segmentation
U-Net
diamond abrasive grits
Online Access:https://www.mdpi.com/2076-3417/12/24/12606

Internet

https://www.mdpi.com/2076-3417/12/24/12606

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