ArticleSensors (Basel, Switzerland)2023
Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
In the industrial sector, tool health monitoring has taken on significant importance due to its ability to save labor costs, time, and waste. The approach used in this research uses spectrograms of airborne acoustic emission data and a convolutional neural network variation called the Residual Network to monitor the tool health of an end-milling machine. The dataset was created using three different types of cutting tools: new, moderately used, and worn out. For various cut depths, the acoustic emission signals generated by these tools were recorded. The cuts ranged from 1 mm to 3 mm in depth. In the experiment, two distinct kinds of wood-hardwood (Pine) and softwood (Himalayan Spruce)-were employed. For each example, 28 samples totaling 10 s were captured. The trained model's prediction accuracy was evaluated using 710 samples, and the results showed an overall classification accuracy of 99.7%. The model's total testing accuracy was 100% for classifying hardwood and 99.5% for classifying softwood.
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Registered trials
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