Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models

Kong, Dongdong; Chen, Yongjie; Li, Ning

doi:10.1007/s00170-017-0367-1

Cited by 31 publications

(16 citation statements)

References 33 publications

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“…All this feature engineering working together with the characterisation of tool wear stages defined in terms of surface finishing were used for training an artificial neural network that achieved 95% accuracy. A more sophisticated approach employing both Gaussian mixture hidden Markov models and back propagation neural networks was presented in [17], where a larger set of statistical features, 18 in total, and correlations have been extracted from force signals. Although these are elegant and robust data-driven methods, the preprocessing and identification of features are the main disadvantages, as these are time consuming and require expert knowledge.…”

Section: Tool Wear Monitoringmentioning

confidence: 99%

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Terrazas

Martínez-Arellano

Benardos

et al. 2018

JMMP

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The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%.

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Section: Tool Wear Monitoringmentioning

confidence: 99%

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Terrazas

Martínez-Arellano

Benardos

et al. 2018

JMMP

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“…Prognostic approaches can be divided into two categories: model-based and data-driven. The first ones rely on the a priori knowledge of the underlying physical laws and probability distributions that describe the dynamic behaviour of a system [5][6][7][8]. Although these have proven to be successful, an in-depth understanding and expertise of the physical processes that lead to tool failure is required.…”

Section: Introductionmentioning

confidence: 99%

Tool wear classification using time series imaging and deep learning

Martínez-Arellano

Terrazas

Ratchev

2019

Int J Adv Manuf Technol

154

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Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

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“…To describe the time-variant transition probability of tool wear states and the state duration dependency, Zhu and Liu [11] proposed a hidden semi-Markov model (HSMM) for online tool wear estimation. By extracting features from the force signal in time domain, Kong et al [12] proposed a Gaussian mixture-hidden Markov model (GMHMM) for tool wear estimation. Zhou and Xue [13] reviewed the state-of-the-art methods of TCM in milling processes, and the monitoring models such as artificial neural network (ANN), HMM, and SVM for the categorization of cutting tool states were discussed.…”

Section: Introductionmentioning

confidence: 99%

“…ese works [4,[6][7][8][9][10][11][12][13][14] solved the problem for recognizing tool wear states in the milling process. However, the models established in these studies are all based on single classifier.…”

Section: Introductionmentioning

confidence: 99%

Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

Yang

Mei

Jiang

et al. 2019

Shock and Vibration

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Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G-mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.

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Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models

Cited by 31 publications

References 33 publications

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Tool wear classification using time series imaging and deep learning

Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

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