FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process

Sevilla-Camacho, P. Y.; Robles-Ocampo, J.B.; Jáuregui-Correa, Juan Carlos; Jimenez-Villalobos, D.

doi:10.1016/j.measurement.2014.12.037

Cited by 35 publications

(9 citation statements)

References 11 publications

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“…Vibration sensors are widely employed in TCM because they are inexpensive, easily installed, and provide similar periodic signal shapes to those of cutting force sensors [21]- [23]. Besmir et al established that the level of vibration generated during the milling process increases with increasing deterioration in the tool condition [24], and the feasibility of adopting vibration signals for TCM in milling processes has been demonstrated by numerous subsequent studies [25]- [28].…”

Section: ) Vibrationmentioning

confidence: 99%

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Zhou

Sun

2020

IEEE Access

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Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time − frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multidomain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors. INDEX TERMS Tool condition monitoring (TCM), milling process, current sensor, kernel extreme learning machine (KELM), angle kernel.

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Section: ) Vibrationmentioning

confidence: 99%

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Zhou

Sun

2020

IEEE Access

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“…To overcome the shortcomings of the time-and-frequency-domain-based methods, a time–frequency analysis method based on a wavelet transform (WT) has been used for feature extraction in milling TCM. In WT-based methods, a discrete wavelet transform (DWT) [ 27 ], a continuous wavelet transform (CWT) [ 28 , 29 ], and a wavelet packet transform (WPT) [ 30 ] have been applied in order to extract a series of wavelet coefficients to reflect the tool state.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Zhou

Wang

2018

Sensors

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Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.

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“…Sevilla et al presented a reconfigurable system using the vibration signals generated from machining tests, which were performed under different tool conditions, and cut parameters for tool condition monitoring in the high-speed machining (HSM) process. 12 Patra et al developed a tool condition (flank wear) monitoring system using the vibration signals of the machining process. 13 They showed that the fuzzy radial basis function based neural network can recognize the features extracted from the time domain by applying the wavelet packet approach, which underlies the vibration signals more effectively than other methods (e.g.…”

Section: Vibration Mode Of a Turning Toolmentioning

confidence: 99%

Condition Monitoring of Single Point Cutting Tools Based on Machine Learning Approach

Gangadhar¹,

Kumar²,

Narendranath³

et al. 2018

IJAV

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This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools.

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FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process

Cited by 35 publications

References 11 publications

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Condition Monitoring of Single Point Cutting Tools Based on Machine Learning Approach

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