Intelligent Chatter Detection in Milling using Vibration Data Features and Deep Multi-Layer Perceptron

Şener, Batıhan; Serin, Gökberk; Gudelek, Mehmet Ugur; Özbayoğlu, A. Murat; Ünver, Hakkı Özgür

doi:10.1109/bigdata50022.2020.9378223

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…The following section summarizes some major challenges in the development of accurate and robust chatter detection systems and future perspectives for further research efforts to address. [89,132,135,136,149,152,153,159,192,199,222,374] Others and ensemble [72,75,134,153,154,182,185,195,196,204,227,239]…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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“…The technique performs milling experiments under different cutting forces and uses adaptive feature analysis and unique particular value entropy to obtain the required data for the experiments. Sener et al [9] proposed a groove milling chatter detection method based on vibration data features. In this experiment, the DMLP algorithm is trained using a fast Fourier transform (FFT) in the context of a deep learning-depth multi-layer perceptron (DMLP).…”

Section: Introductionmentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

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“…Deep learning methods are based on feature learning, and many studies have been done that use neural networks to identify chatter. 21 The multilayer perceptron (MLP) has been employed for chatter detection by using tool vibration data, 22 and neural networks are often trained using preprocessed, rather than raw noisy data, because the extraction of well-organized features is easier. Continuous wavelet transform can also be applied to preprocessed data for training a deep convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

Machine tool chattering monitoring by Chen-Lee chaotic system-based deep convolutional generative adversarial nets

Kuo

Luan

Tseng

et al. 2023

Structural Health Monitoring

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Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. In this study, an effective procedure for chatter data preprocessing is proposed that can improve neural network learning results from data of extremely low quantity. Different Computer numerical control (CNC) machines, cutting tools, operation conditions as well as workpiece material, and shapes all generate different dynamic behavior . Therefore, the same cutting conditions are processed in different CNCs, some will produce chatter, and some will not. In order to collect chatter signals of different cutting environments, the cost of materials and time is relatively high. Cutting chatter also leads to tool wear, which also increases the cost of data collection. This makes the use of CNCs for large-scale chatter testing experiments impractical. However, a way of producing accurate chatter test results from rather sparse data is needed. The solution to this practical problem involved an innovative data preprocessing and training strategy combined with a modified convolutional neural network and a deep convolutional generative adversarial net. Through the characteristics of a chaotic attractor, the variability of chatter data can be minimized. Moreover, the characteristics of a chaotic attractor are utilized where the chaotic system, very sensitive to the input, can distinguish data with chatter and without chatter to improve chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. Original training data are collected and preprocessed by the Chen-Lee chaotic error mapping. Experimental results indicated that the generative adversarial network (GAN) model could generate better training data than the traditional data augmentation method. The convolutional neural networks were trained using augmented data produced by the generator network. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results are compared without a data generator and data augmentation. Using only 60 original data, the proposed method has an accuracy of 95.3% on leave-one-out cross-validation over 10 runs and surpassed other methods and models. The forged data are also compared with original training data as well as data produced by augmentation. The distribution shows that forged data have similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model.

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Intelligent Chatter Detection in Milling using Vibration Data Features and Deep Multi-Layer Perceptron

Cited by 10 publications

References 27 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Machine tool chattering monitoring by Chen-Lee chaotic system-based deep convolutional generative adversarial nets

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