A novel monitoring method for turning tool wear based on support vector machines

Yang, Songsong; Wang, Shilong; Yi, Lili; Xue, Haitao; Cao, Yang; Sun, Shouli

doi:10.1177/0954405416645998

Cited by 17 publications

(9 citation statements)

References 29 publications

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“…Case 1 is designed for the tool wear prediction with less features while Case 2 is designed for the tool wear prediction with more measured features. The details of the experimental setup can refer to Yang et al 27…”

Section: Evaluation Criteriamentioning

confidence: 99%

Tool wear monitoring using generalized regression neural network

Kang

Wang

et al. 2019

Advances in Mechanical Engineering

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To improve the modeling accuracy and efficiency of the tool wear monitoring system, a generalized regression neural network is adopted to build the tool wear prediction model because its excellent performance on learning speed and fast convergence to the optimal results whether the sample data are small or large. The low predictive accuracy and efficiency are caused by traditionally manual adjustment of the spread parameters in generalized regression neural network and then the improved fruit fly optimization algorithm is proposed to optimize the spread parameters of regression neural network automatically. Combining the improved fruit fly optimization and generalized regression neural network, the tool wear prediction method is proposed in the paper. Various experiments are carried out to validate the proposed method and the comparison results show a good agreement. In addition, the proposed method is compared to the tool wear prediction method in the literature, and the comparison results also show that the proposed method can achieve better performance.

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Section: Evaluation Criteriamentioning

confidence: 99%

Tool wear monitoring using generalized regression neural network

Kang

Wang

et al. 2019

Advances in Mechanical Engineering

Self Cite

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show abstract

“…Due to the complexity of tool wear during machining process, the establishment of tool wear mechanism models is more and more challenging, while data-driven methods can learn data-driven models from a large volume of data, and the data-driven model can be equivalent to complex mechanism models within certain range of error, so data-driven method provides a new idea for accurate tool wear prediction. 13–18…”

Section: Related Workmentioning

confidence: 99%

An accurate cutting tool wear prediction method under different cutting conditions based on continual learning

Hua

Mou

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Cutting tool wear prediction plays an important role in the machining of complex aerospace parts, and it is still a challenge under varying cutting conditions. To overcome the limitations of the existing methods in generalization ability when dealing with cutting conditions changing largely, this paper proposed a novel cutting tool wear prediction method based on continual learning. A meta-LSTM model is firstly trained for specific cutting conditions and can be easily fine-tuned with very small number of samples to adapt to new cutting conditions. Specifically, the meta-model could be continuously updated as machining data increase by using an orthogonal weights modification method. The experiment results show that the proposed method can realize accurate prediction of tool wear under different cutting conditions. Compared with existing methods including meta-learning methods, the range of adapted cutting conditions could be expanded as the task distribution of new cutting conditions is continuously learned by the prediction model.

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“…The SVR model allows for the deviation of ε between f ( x ) and y i , only when f ( x ) − y i > 0 , the loss is calculated. 27–32 For the regression problem, the SVR problem can be transformed into…”

Section: Feature Extraction and Intelligent Algorithmmentioning

confidence: 99%

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

Cheng

Shi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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In the process of high strength steel turning, tool wear will reduce the surface quality of the workpiece and increase cutting force and cutting temperature. To obtain the fine surface quality and avoid unnecessary loss, it is necessary to monitor the state of tool wear in the dry turning. In this article, the cutting force, vibration signal and surface texture of the machined surface were collected by tool condition monitoring system and signal processing techniques are being used for extracting the time-domain, frequency-domain and time-frequency features of cutting force and vibration. The gray level processing technique is used to extract the features of the gray co-occurrence matrix of the surface texture and found that these features changed simultaneously when the cutting tool broke. After this, an intelligent prediction model of tool wear was built using the support vector regression (SVR) whose kernel function parameters were optimized by the grid search algorithm (GS), the genetic algorithm (GA) and the particle swarm optimization algorithm respectively. The features extracted from the signals and surface texture are used to train the prediction model in MATLAB. It was found that after the surface texture features were fused using the intelligent prediction model on the basis of the features of cutting force and vibration, prediction accuracy of the proposed method is found as 97.32% and 96.72% respectively under the two prediction models of GA-SVR and GS-SVR. Moreover, the intelligent prediction model can not only predict the tool wear under different cutting conditions, but also the different wear stages in a single wear cycle and the absolute error between the predicted value and the actual value is less than 10 μm, the confidence coefficient of prediction curve is around 0.99.

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A novel monitoring method for turning tool wear based on support vector machines

Cited by 17 publications

References 29 publications

Tool wear monitoring using generalized regression neural network

Tool wear monitoring using generalized regression neural network

An accurate cutting tool wear prediction method under different cutting conditions based on continual learning

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

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