Tool wear identification and prediction method based on stack sparse self-coding network

Qin, Yiyuan; Liu, Xianli; Yue, Caixu; Zhao, Mingwei; Wei, Xudong; Wang, Lihui

doi:10.1016/j.jmsy.2023.02.006

Cited by 49 publications

(7 citation statements)

References 23 publications

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“…The data were obtained from the industrial milling tool wear prediction dataset published by the American Fault Diagnosis and Health Management Association in 2010 [42]. The experimental platform was the CNC milling machine (Röders Tech RFM760).…”

Section: The First Experimental Datamentioning

confidence: 99%

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

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Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELM) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, PSO-LSSVM, LSTM, SSAE, RNN, and DBO-DELM.

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Section: The First Experimental Datamentioning

confidence: 99%

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

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“…Extracting features on frequency domain can also be used to assess the cutting-tool wear conditions [27]. Therefore, extracting features from different domains in the raw signals can effectively increase the relevance to cutting-tool wear [28,29]. In this study, feature extraction was performed on the cleaned cutting force signals, with their expressions, as indicated in Table 4.…”

Section: Data Processingmentioning

confidence: 99%

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

Lin,

Hsieh

2024

Applied Sciences

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This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

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“…The direct method uses contact sensors or precision optical measuring instruments to directly obtain the tool wear shape for further analyze, with computer vision being a commen approch [12], [13]. This method provides accurate and intuitive wear information but requires stopping the machining process, which cannot be monitored in real-time [14]. Indirect measurement is a method to infer the tool state by measuring the sensor signal data, like vibration, force [15], secondary electron signals [16], [17].…”

Section: Introductionmentioning

confidence: 99%

An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing

Dong,

Zhao

2024

IEEE Access

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Computer numerical control (CNC) machine tools play a crucial role in the manufacturing industry, and cutting tools, as key functional components, directly impact the quality of the machining process. An improved autoEncoder with multi-head attention for tool wear prediction is proposed. MultiCNN-Attention-GRU (MCAG) consists of an encoder and decoder. The encoder contains multiple sets of Convolutional Neural Networks (CNNs) and CNNs adaptively extract signal features. The decoder includes Multi-Head Attention (MHA) and Gated Recurrent Unit (GRU), which can adaptively enhance the relevant feature weights and extract long-term, deep different features. For the model training, a monotonicity loss function is defined. The proposed method is validated on the 2010 PHM Data Challenge (PHM2010) public dataset. The original dataset is dimensionally reduced and then resampled. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an Mean Absolute Error (MAE) of 6.15 and Mean Square Error (MSE) of 79.6, which is approximately 1.6 and 13 lower than the second-place algorithm. The result validates the superior performance of the proposed model compared to other deep learning algorithms in predicting tool wear.

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Tool wear identification and prediction method based on stack sparse self-coding network

Cited by 49 publications

References 23 publications

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing

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