Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering

Sevilla-Camacho, P. Y.; Robles-Ocampo, J.B.; Muñiz, Jesús; Lee-Orantes, F.

doi:10.1007/s00170-015-7302-0

Cited by 31 publications

(6 citation statements)

References 11 publications

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“…Machining signals are captured using different sensors like vibration, current, dynamometer, acoustic etc., to monitor the tool wear. The vibration sensor captures the vibration generated due to cutting forces during the machining (Sevilla-Camacho et al, 2015). Many studies show that the vibration sensor is feasible for the tool wear prediction (Chen et al, 2011;Wang et al, 2014).…”

Section: Background Studymentioning

confidence: 99%

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

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Purpose Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.Design/methodology/approachThis paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.FindingsThe R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.Originality/value The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

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Section: Background Studymentioning

confidence: 99%

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

View full text Add to dashboard Cite

show abstract

“…The monitoring results are easily interfered with by cutting fluid and chips, so the direct method is not suitable for the machine tool processing site [ 12 , 13 ]. The indirect method collects sensor signals, such as force [ 14 , 15 ], vibration [ 16 , 17 ] and acoustic emission [ 13 ], extracts data features and establishes a feature map relationship between monitoring signals and tool wear condition.…”

Section: Introductionmentioning

confidence: 99%

An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing

Dong

Wang

Yang

et al. 2023

Sensors

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Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor.

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“…However, due to the practical limitation of accessibility, the lack of illumination and the presence of coolant liquid, using direct TCM is very difficult in realtime monitoring [9]. The indirect measurement method is based on various inputs, such as cutting force [10,11], vibration [12,13], motor current [14,15], acoustic emission [16,17], temperature [18,19], collected by different sensors to monitor cutting in real-time. It involves the analysis of signals to determine the current work status of in-service equipment to predict possible failures.…”

Section: Introductionmentioning

confidence: 99%

A Method for Mill Monitoring Based on Inter-insert Periodic Correlation Using Singular Value Decomposition (SVD)

Zhu

Girardin

Antoni

2021

Preprint

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This paper introduces a method to monitor the wear of end milling tools in real-time production based on inter-insert periodic correlation. The aim is to detect abnormal behavior of the cutter as early as possible to prevent severe tool failure and subsequent losses. The approach takes advantage of the angular domain to segment the signal in periodic cycles of the same angular duration, which are then amenable to correlation analysis. An ordered separability index with latent correlation characteristics is proposed to assess the current operating state of the tool. A series of simulations with existing experimental data are run to test the feasibility of the proposed index and to calculate the corresponding confidence interval. This approach has a high potential to form an efficient tool condition monitoring system. Compared to the traditional teach-in method, this method is more independent of the cutting conditions (changes of velocity or direction) and has no requirement for a trial cut, making the method useful for small batch production and can reduce the rate of false alarms.

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Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering

Cited by 31 publications

References 11 publications

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing

A Method for Mill Monitoring Based on Inter-insert Periodic Correlation Using Singular Value Decomposition (SVD)

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