How does real-time use vision affect the accuracy of surface roughness prediction in a model?
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Real-time use of vision has been shown to improve the accuracy of surface roughness prediction in models. In one study, a deep learning model based on optical images was proposed for surface roughness prediction after coating removal . The model, called SSEResNet, effectively extracted detailed features from the optical images and outperformed other CNN backbone networks in terms of prediction accuracy. Another study utilized real-time monitoring of main drive power in face milling processes to predict surface roughness deviations . The use of artificial intelligence methods, such as random forest, regression trees, and radial-based functions, allowed for accurate prediction of surface roughness based on process parameters and tool wear. These methods provided visual information and thresholds for avoiding rapid tool wear, enhancing the accuracy of surface roughness prediction .
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21 Citations | The provided paper does not mention the use of real-time vision or its effect on the accuracy of surface roughness prediction in the model. |
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| The provided paper does not mention anything about real-time use vision or its effect on the accuracy of surface roughness prediction in a model. | |
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| The provided paper does not mention anything about real-time use vision or its effect on the accuracy of surface roughness prediction. |
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