Tool wear prediction based on parallel dual-channel adaptive feature fusion

Yang, Jinfei; Wu, Jinxin; Li, Xianwang; Qin, Xuejing

doi:10.1007/s00170-023-11832-0

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…To more comprehensively assess the performance of the model, PR-AUC [36], CGRU-IConvGRU-A [37], ConvLSTM-Att [24], and MDMCNN-BiLSTM [38] were used as benchmarks for comparison. The experimental settings follow those described in the original literature, using MAE and R 2 as performance metrics.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach

Guo,

Wang

2024

Machines

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In the manufacturing sector, tool wear substantially affects product quality and production efficiency. While traditional sequential deep learning models can handle time-series tasks, their neglect of complex temporal relationships in time-series data often leads to errors accumulating in continuous predictions, which reduces their forecasting accuracy for tool wear. For addressing these limitations, the parallel convolutional and recurrent neural networks with attention-modulated residual learning (ParaCRN-AMResNet) model is introduced. Compared with conventional deep learning models, ParaCRN-AMResNet markedly enhances the efficiency and precision of feature extraction from time-series data through its innovative parallel architecture. The model adeptly combines dilated convolution neural network and bidirectional gated recurrent units, effectively addressing distance dependencies and enriching the quantity and dimensions of extracted features. The strength of ParaCRN-AMResNet lies in its refined ability to capture the complex dynamics of time-series data, significantly boosting the model’s accuracy and generalization capability. The model’s efficacy was validated through comprehensive milling experiments and vibration signal analyses, showcasing ParaCRN-AMResNet’s superior performance. In evaluation metrics, the model achieved a MAE of 2.6015, MSE of 15.1921, R2 of 0.9897, and MAPE of 2.7997%, conclusively proving its efficiency and accuracy in the precise prediction of tool wear.

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Section: Comparative Experimentsmentioning

confidence: 99%

Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach

Guo,

Wang

2024

Machines

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“…Long Short-Term Memory networks (LSTMs), a more advanced form of RNNs, offer improvements but they still struggle with processing high-rate signals, such as the ones common in industrial applications. For this reason, they must typically be combined with a feature extraction step [ 21 , 22 ], focusing on learning temporal dependencies between features instead of the elements of the raw input.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Stathatos,

Tzimas,

Benardos

et al. 2024

Sensors

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This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators—average roughness, peak-to-valley roughness, and diameter deviation—are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model’s ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

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A novel tool life model for varying process conditions and cutting volumes using cutting power consumption

Lee,

Yoon

2024

Int J Adv Manuf Technol

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Tool wear prediction based on parallel dual-channel adaptive feature fusion

Cited by 8 publications

References 42 publications

Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach

Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

A novel tool life model for varying process conditions and cutting volumes using cutting power consumption

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