Tool flank wears estimation by simplified SVD on emitted sound signals

Prakash, Kritika; Samraj, Andrews

doi:10.1109/icedss.2017.8073702

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Zhou et al [13] monitored tool wear acoustics using a two-layer angular kernel Extreme Learning Machine (ELM), utilizing a subset of acoustic sensor parameters for wear condition identification. Prakash and Samraj [14] applied Singular Value Decomposition (SVD) to analyze acoustic signals for tool wear monitoring in close-gap turning. Vibration, akin to cutting force, also exhibits strong sensitivity to tool conditions.…”

Section: Introductionmentioning

confidence: 99%

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Ni,

Liu,

Meng

et al. 2023

Machines

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Tool wear is a crucial factor in machining as it directly impacts surface quality and indirectly decreases machining efficiency, which leads to significant economic losses. Hence, monitoring tool wear state is of the utmost importance for achieving high performance and efficient machining. Although monitoring tool wear state using a single sensor has been validated in laboratory settings, it has certain drawbacks such as limited feature information acquisition and inability to learn important features adaptively. These limitations pose challenges to quickly extending the monitoring function of tool wear state of the machine tools. To solve these problems, this paper proposes a double-attention deep learning network based on vibroacoustic signal fusion feature infographics. The first solution is the construction of novel infographics using tool-intrinsic characteristics and multi-domain fusion features of multi-sensor inputs, which includes correlation analysis, principal component analysis, and feature fusion. The second solution is to build a novel deep network with a double-attention module and a spatial pyramid pooling module which can accurately and quickly identify tool wear state by successfully extracting critical spatial data from the infographics at various scales. The validity of the network is examined through an interpretability analysis based on the class activation graph. In terms of the tool wear status recognition task, the F1 score of the double-attention model based on an information graph is 11.61% higher than Resnet18, and peak recognition accuracy reaches 97.98%.

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Section: Introductionmentioning

confidence: 99%

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Ni,

Liu,

Meng

et al. 2023

Machines

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“…Hilbert-Huang transformation also helps to estimate the results to a good extent. [10] A continuous enhancement of the process leads to much simpler method and effective results and is presented by Prakash & A.Samraj [11,12,13] Figure1: Different types of tool bits used in the cutting process In alignment to Industry 4.0 automation a Machine learning approach seems to be appropriate to address this problem. Hence we started a basic ML approach using a simple Linear Regression and found the approach results in fruitful but different way of representations.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Condition Monitoring Using Emitted Sound Signals By Simple Machine Learning Technique

Perumal¹,

Bhadrinathan²,

Samraj³

2022

Design, Construction, Maintenance

Self Cite

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As a continuous enhancement to the tool wear monitoring using non-disturbing method of sound wave analysis, a simple machine learning technique enhances the prediction to better levels and reduces the procedures. A simple linear regression Algorithm was used to train and predict the trends of various degrees of tool wear to distinguish them from each other. The results based on this simple linear regression were successful in showing the difference of sound patterns and are reported.

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“…One of the first applications was the detection and study of tool and workpiece contact in machining [7,8], which quickly led to an interest in the automatic detection of malfunctions. In the event of a tool with broken or blunt teeth, acoustic emission technologies can automatically detect their presence with the use of relatively simple setups [9][10][11][12][13]. Even if the tool is in good working condition, phenomena like runout [14,15] or chatter [16][17][18][19] are common problems that modern industry has to face, but due to the change in the acoustic emission generated by each of those phenomena they can be readily detected and solved.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

Sio-Sever

Leal-Muñoz

Lopez-Navarro

et al. 2020

Sensors

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This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.

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Tool flank wears estimation by simplified SVD on emitted sound signals

Cited by 6 publications

References 8 publications

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Tool Wear Condition Monitoring Using Emitted Sound Signals By Simple Machine Learning Technique

Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

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