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Tool wear conditions impact the surface quality of the workpiece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prom

Externí odkaz: http://arxiv.org/abs/2407.01211

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Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation

Autor: Li, Zongshuo, Meurer, Markus, Bergs, Thomas

As an integral part of contemporary manufacturing, monitoring systems obtain valuable information during machining to oversee the condition of both the process and the machine. Recently, diverse algorithms have been employed to detect tool wear using

Externí odkaz: http://arxiv.org/abs/2407.01200

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Deep Learning Based Tool Wear Estimation Considering Cutting Conditions

Autor: Li, Zongshuo, Meurer, Markus, Bergs, Thomas

Tool wear conditions impact the final quality of the workpiece. In this study, we propose a deep learning approach based on a convolutional neural network that incorporates cutting conditions as extra model inputs, aiming to improve tool wear estimat

Externí odkaz: http://arxiv.org/abs/2407.01199

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