Acoustic Signal Analysis for Prediction of Flank Wear During Conventional Milling

Roney, Travis; Bauccio, Anthony; Shaffer, Derek; Lorson, Paige; Ragai, Ihab; Loker, David; Nikhare, Chetan P.

doi:10.1115/imece2018-86886

Cited by 7 publications

(2 citation statements)

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“…The most critical task here is identifying the tool's wear threshold from a permissible to a non-permissible limit [5]. Many researchers have approached this problem in the past using various sensing modalities such as vibration [6], force [7], data fusion [8], acoustics [9], and motor current [10]. Recent work involves using acoustic emission sensors in monitoring tool wear as the frequency range of acoustic sensors is higher than the vibration and background noise [11].…”

Section: Introduction and Overviewmentioning

confidence: 99%

Tool Wear Prediction in Milling: A Comparative Analysis Based on Machine Learning and Deep Learning Approaches

Kamat¹,

Nargund²,

Kumar³

et al. 2022

IJCDS

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The milling machine's cutting tool is a vital asset; its breakdown results in unplanned downtime, which reduces industrial efficiency.Tool-Wear Monitoring(TWM) is one of the primary goals of the manufacturing industry due to the manifold benefits it provides, such as optimizing production efficiency, improving performance, and increasing the life of the tool. Most of the work carried out in this domain involves statistical-based techniques, which require expert domain knowledge in formulating degradation models of the tools. Data-driven machine learning and deep learning models have recently been used to analyze tool wear data and make efficient predictions about its remaining useful life. This paper presents a comparative approach to tool wear monitoring using the clustering machine learning technique of K-Nearest Neighbour (k-NN) and deep learning technique of Convolutional Neural Network (CNN) and hybrid Autoencoder-LSTM (AE-LSTM) models. The CNN and AE-LSTM techniques out-perform k-NN by achieving a higher degree of separability of around 93% and 87%, respectively, as per the ROC-AUC values. The techniques provide improved outcomes in terms of precision, recall, and f1-score, indicating that the models are more accurate at detecting false positives.

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Section: Introduction and Overviewmentioning

confidence: 99%

Tool Wear Prediction in Milling: A Comparative Analysis Based on Machine Learning and Deep Learning Approaches

Kamat¹,

Nargund²,

Kumar³

et al. 2022

IJCDS

View full text Add to dashboard Cite

show abstract

“…Thenceforth, the capability of AE sensors to monitor and detect various machining attributes including tool wear, minimum uncut chip thickness, surface quality and roughness, precision shaping, cutting energy assumption, tool breakage, chatter vibration, chip tangling, and tool life has been investigated. [5][6][7][8][9][10][11][12][13][14][15][16][17][18] Iwata and Moriwaki 8 were the first to use AE signal information to assess the tool wear mode in the cutting process. They stated that the AE power increases up to 350 kHz with tool wear, and then saturation is achieved.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

Anahid

Heydarnia

Niknam

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

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Optimal machine learning for detecting lathe machining parameters

Rall

Loker

Nikhare

2023

Int J Adv Manuf Technol

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Acoustic Signal Analysis for Prediction of Flank Wear During Conventional Milling

Cited by 7 publications

References 0 publications

Tool Wear Prediction in Milling: A Comparative Analysis Based on Machine Learning and Deep Learning Approaches

Tool Wear Prediction in Milling: A Comparative Analysis Based on Machine Learning and Deep Learning Approaches

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

Optimal machine learning for detecting lathe machining parameters

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