An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion

Liu, Xianli; Zhang, Bowen; Li, Xuebing; Liu, Shaoyang; Yue, Caixu; Liang, Steven Y.

doi:10.1007/s10845-022-01954-9

Cited by 45 publications

(7 citation statements)

References 40 publications

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“…Additionally, figures 6(d)-(f) Furthermore, to demonstrate the superior performance of the proposed method, we conduct comparative experiments with six advanced prediction methods in the field. These methods include DBN [43], TDConvLSTM [44], TBNN [28], CTNN [45], HACDNet-GRU [30], and TCNA [33]. They utilize various modules such as LSTM, TCN, attention mechanisms, and GRU, and have achieved accurate prediction accuracy in tool wear domain.…”

Section: Results Of Phm2010mentioning

confidence: 99%

“…Therefore, the gated recurrent unit (GRU) with two-gate structure, which has the advantages of simple structure and fast training speed, has emerged and been used to extract deeper temporal features [29]. Liu et al [30] built a depth-gated recurrent unit for extracting time-series features hidden in sequences. After that, temporal convolutional network (TCN) is an algorithm for extracting temporal features in parallel, which has the advantage of fast computation with little training difficulty and better performance than RNN and LSTM [31,32].…”

Section: Introductionmentioning

confidence: 99%

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A multi-model method for tool wear prediction with deep temporal features and correlation alignment

Dong,

Chen,

Gao

et al. 2023

Meas. Sci. Technol.

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Accurate prediction of tool wear is essential to ensure the machining quality of parts. However, in the actual milling process, the data distribution varies greatly between sensor signals due to variations in individual tools and machining parameters; moreover, a single deep learning model is less reliable when processing a large volume of signals. All these problems make accurate tool wear prediction challenging. Therefore, we propose a multi-model method with two-stage. In the first stage, the tool wear data is initially divided into two parts. For each part, we design a correlation-aligned multiscale convolutional temporal attention gated recurrent neural network model to perform preliminary prediction, aiming at extracting the deep temporal features from diverse signals and mitigating the sensitivity of the features to the changes in data distributions. In the second stage, we adaptively aggregate the preliminary prediction from multiple models to obtain the final prediction via a joint decision-making module to extend the decision boundary of single model and improve the tool wear prediction performance. Finally, two sets of experiments are conducted for different tools and machining conditions. The experimental results show that our proposed method significantly reduces the root mean square error (RMSE) by 15% and the mean absolute error (MAE) by 18% compared to other methods.

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Section: Results Of Phm2010mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-model method for tool wear prediction with deep temporal features and correlation alignment

Dong,

Chen,

Gao

et al. 2023

Meas. Sci. Technol.

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“…He et al [41] used stacked sparse autoencoders to improve tool wear prediction performance. Liu et al [42] and Xu et al [43] developed [60]. Another is that testing samples obey the same data distribution as training samples [61].…”

Section: Literature Reviewmentioning

confidence: 99%

Tool Wear Prediction Based on Multi-Information Fusion and Genetic Algorithm-Optimized Gaussian Process Regression in Milling

Huang

Shao

Guo

et al. 2023

IEEE Trans. Instrum. Meas.

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Tool wear condition monitoring plays a crucial role in intelligent manufacturing systems to enhance machining quality and efficiency. The indirect methods employ various sensor signals to monitor tool wear condition, attracting wide attention in industrial applications. Multi-information fusion technologies can promote tool wear monitoring results to be more accurate and reliable. For improving the prediction accuracy and ensuring the reliability of the indirect methods, this study proposes a tool wear prediction method based on multi-information fusion and genetic algorithm (GA)-optimized Gaussian process regression (GPR). First, wavelet packet denoising (WPD)-based signal processing is adopted to suppress the noise interference of multisensor signals. Then, kernel principal component analysis (KPCA)-based dimension reduction is employed to mine the most sensitive features to flank wear from candidate multidomain features. Next, a fusion model of GPR and GA optimization is designed to establish a nonlinear mapping relationship between sensitive characteristics and flank wear width. Finally, performance evaluations under three sets of milling tests are carried out to validate the effectiveness of the proposed method. Experimental results indicate that the proposed method can lower prediction error and uncertainty of flank wear width compared with other intelligent approaches, promoting a successful application of indirect monitoring methods in milling.

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“…Liu et al [19] extracted local features using parallel Residual Network (ResNet) and then utilized a stacked bidirectional LSTM network to extract time-series features for predicting tool wear. Liu et al [20] extracted local features from multi-sensor signals using the improved DenseNet algorithm, followed by temporal feature extraction with the GRU algorithm, employing two deep learning models for tool wear prediction. Cheng et al [21] monitored tool status by combining parallel CNN and bidirectional LSTM models.…”

Section: Introductionmentioning

confidence: 99%

Research on tool remaining useful life prediction algorithm based on machine learning

Ge,

Teo,

Moey

et al. 2024

Eng. Res. Express

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Tool wear during machining significantly impacts workpiece quality and productivity, making continuous monitoring and accurate prediction essential. In this context, the present study develops an efficient tool wear prediction system to enhance production reliability and reduce tool costs. It is worth noting that conventional methods, including support vector regression, autoencoders, attention mechanisms, CNNs, and RNNs, have limitations in feature extraction and efficiency. Aiming at resolving these limitations, a multiscale convolutional neural network (MDCNN)-based algorithm is proposed for predicting the remaining life of milling cutters. The algorithm uses preprocessing techniques like wavelet transform and principal component analysis for noise reduction and feature extraction. It then extracts temporal data features using convolutional layers of different scales and employs a self-attention mechanism for feature encoding. Validation on the PHM2010 milling cutter wear dataset with 10-fold cross-validation demonstrates that the MDCNN model achieves a wear prediction accuracy of 97%, a recall rate of 98%, and an F1 score of 97%. The MDCNN model effectively processes multi-band data and captures complex temporal features, confirming its efficiency and accuracy in predicting milling cutter wear and remaining service life.

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An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion

Cited by 45 publications

References 40 publications

A multi-model method for tool wear prediction with deep temporal features and correlation alignment

A multi-model method for tool wear prediction with deep temporal features and correlation alignment

Tool Wear Prediction Based on Multi-Information Fusion and Genetic Algorithm-Optimized Gaussian Process Regression in Milling

Research on tool remaining useful life prediction algorithm based on machine learning

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