Multisensor-based tool wear diagnosis using 1D-CNN and DGCCA

Yin, Yong; Wang, Shuxin; Zhou, Jian

doi:10.1007/s10489-022-03773-0

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…In this model, they used the transformer model and convolutional neural networks together to obtain condition monitoring data such as shear force in parallel. Yin et al [60] performed multi-sensor-based tool wear detection using onedimensional CNN and deep generalized canonical correlation analysis. Experiments showed that their performed approach can acquire sufficient accuracy and real-time implementation.…”

Section: Eksploatacja I Niezawodnosc -Maintenance and Reliabilitymentioning

confidence: 99%

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Karabacak¹

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

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Section: Eksploatacja I Niezawodnosc -Maintenance and Reliabilitymentioning

confidence: 99%

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Karabacak¹

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…The advantage of indirect detection methods is that they are non-intrusive and provide a more holistic understanding of the overall milling process conditions, allowing for early identification of undesired changes or indications of cutter damage. Thus, suspicious changes in cutter performance can be identified, enabling timely interventions such as replacing worn cutters or adjusting parameters to minimize unforeseen downtime, enhance product quality, and improve overall productivity [12].…”

Section: Introductionmentioning

confidence: 99%

Innovative Tool Condition Classification: Utilizing Time-Frequency Moments as Inputs for BiLSTM Networks in Milling Processes

Rahman,

Jauhari,

Huda

et al. 2024

Preprint

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Milling is one of the most important processes in the manufacturing industry, and it uses rotating cutting tools to sculpt raw materials into intricate shapes and structures. However, tool wear and breakage present significant challenges influenced by various factors, such as machining parameters and tool fatigue, which directly impact surface quality, dimensional accuracy, and production costs. Therefore, monitoring cutter wear conditions is essential for ensuring milling process efficiency. This study proposes applying BiLSTM networks to classify end mill tool conditions based on vibration signals. Significant improvements in classification accuracy are achieved by extracting features and utilizing spectrogram analysis. Specifically, utilizing dual spectral vibration signals increases the BiLSTM's average accuracy from 84.5–96.3%. These findings demonstrate the effectiveness of the proposed method for real-time tool condition monitoring in milling operations, offering potential benefits for manufacturing processes.

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“…A multi-frequency-band feature extraction structure based on a deep convolution neural network structure was introduced to predict tool wear conditions. Yong et al [23] proposed a one-dimensional convolutional neural network (1D-CNN) and deep generalized canonical correlation analysis (DGCCA). In particular, 1D-CNN was used to extract features from 1D raw data, whereas DGCCA with attention mechanism was used to fuse the feature output from each 1D-CNN.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Li,

Lei,

You

et al. 2024

Machines

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Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE.

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Multisensor-based tool wear diagnosis using 1D-CNN and DGCCA

Cited by 20 publications

References 28 publications

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Innovative Tool Condition Classification: Utilizing Time-Frequency Moments as Inputs for BiLSTM Networks in Milling Processes

Tool Wear Prediction Based on Residual Connection and Temporal Networks

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