Recently, our prodigious PhD student, Jawad Mahmood, wrote an article about machining tool wear prediction using machine learning. Specifically, he applied convolutional neural networks on spectrograms and scalograms of worn tool data. His approach is nice and his work was exhaustive. He published in the prestigious international journal of advanced manufacturing technology, Springer Nature. Here is a link to his paper. Please peruse.
Detection of tool wear during machining by designing a novel 12-way 2-shot learning model by applying L2-regularization and image augmentation – The International Journal of Advanced Manufacturing Technology
Tool wear monitoring is regarded as an incredibly important aspect of improving the surface integrity of machined components in the manufacturing sector. This research study performed operations using twelve different types of drilling and milling tools. The worn tools ranging from grade-1 to grade-5 were categorized based on tool wear severity by measuring the flank wear land width of each tool.
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Tool Wear Detection Using Machine Learning by Psyops Prime is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.