Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

Song, Kaiyu; Wang, Min; Liu, Liming; Wang, Chen; Zan, Tao; Yang, Bin

doi:10.1007/s00170-020-05587-1

Cited by 22 publications

(14 citation statements)

References 27 publications

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“…In both [7] and [6], machining parameters, e.g., speed rotation, are used along with the sensory features to feed the learning model. The approach presented in [19] is based on removing the component related to the operating conditions from the original spindle current signal, and then feeding the residuals to the Deep Convolutional Neural Network (DCNN) that in turn classifies different tool states. In [35], four model coefficients of the cutting force signal are shown to be independent of the operating conditions while being correlated with the tool wear.…”

Section: Related Workmentioning

confidence: 99%

“…In [35], four model coefficients of the cutting force signal are shown to be independent of the operating conditions while being correlated with the tool wear. However, both the approaches in [19] and [35] are based on particular sensor signals, which limits their applicability with other sensor types.…”

Section: Related Workmentioning

confidence: 99%

“…The BMUs corresponding to the input vectors are identified as in (16). The weight vectors are updated as in (19).…”

Section: Som-based Anomaly Detection a Background About Sommentioning

confidence: 99%

“…Hence, the learning model that fits data under different operating conditions will show robustness on the shop floor where different, non-stationary operating conditions exist [6], [18]. Even though the operating conditions cannot be indicative of the system condition, their significant role in the predictive maintenance stems from the fact that they influence the system degradation rate [17], [19] as well as the variables that are used to monitor the system health [17], [20]. Three main issues arise when building a multi-operating-condition model, i.e., a model that is trained to represent data from different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

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A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Assafo

Langendoerfer

2021

IEEE Access

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Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses the issues associated with multi-operating-condition TCM models, namely the varying discrimination ability of sensory features; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The BMUs corresponding to the input vectors are identified as in (16). The weight vectors are updated as in (19).…”

Section: Som-based Anomaly Detection a Background About Sommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Assafo

Langendoerfer

2021

IEEE Access

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show abstract

“…17 and 18, respectively. ) 18) where N represents the number of samples in the test data sets, yi represents the i-th true tool wear value, and y p i represents the i-th predicted tool wear value. Root mean square…”

Section: Performance Appraisal Of Adscnn Modelmentioning

confidence: 99%

Tool Wear Prediction Based on Multidomain Feature Fusion by Attention-Based Depth-Wise Separable Convolutional Neural Network in Manufacturing

et al. 2021

Preprint

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Tool wear during machining has a great influence on the quality of machined surface and dimensional accuracy. Tool wear monitoring is extremely important to improve machining efficiency and workpiece quality. Multidomain features (time domain, frequency domain and time-frequency domain) can accurately characterise the degree of tool wear. However, manual feature fusion is time consuming and prevents the improvement of monitoring accuracy. A new tool wear prediction method based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network is proposed to solve these problems. In this method, multidomain features of cutting force and vibration signals are extracted and recombined into feature tensors. The proposed hypercomplex position encoding and high dimensional self-attention mechanism are used to calculate the new representation of input feature tensor, which emphasizes the tool wear sensitive information and suppresses large area background noise. The designed depth-wise separable convolutional neural network is used to adaptively extract high-level features that can characterize tool wear from the new representation, and the tool wear is predicted automatically. The proposed method is verified on three sets of tool run-to-failure data sets of three-flute ball nose cemented carbide tool in machining centre. Experimental results show that the prediction accuracy of the proposed method is remarkably higher than other state-of-art methods. Therefore, the proposed tool wear prediction method is beneficial to improve the prediction accuracy and provide effective guidance for decision making in processing.

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Deploying Optical Character Recognition to Improve Material Handling and Processing

Shahin,

Hosseinzadeh,

Chen

et al. 2023

Lecture Notes in Mechanical Engineering

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Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

Cited by 22 publications

References 27 publications

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Tool Wear Prediction Based on Multidomain Feature Fusion by Attention-Based Depth-Wise Separable Convolutional Neural Network in Manufacturing

Deploying Optical Character Recognition to Improve Material Handling and Processing

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