A tool wear monitoring approach based on triplet long short-term memory neural networks

Abstract

Advancements in artificial intelligence have significantly improved the monitoring of tool wear in machining processes, thereby enhancing the overall quality of machining. However, the scarcity of tool wear samples poses a challenge to the enhancement of model precision. This necessitates the exploration of monitoring techniques that are effective even with small sample sizes. A method involving a triplet long short-term memory (LSTM) neural network is introduced, which offers the potential for superior accuracy even with limited training data. During the machining process, spindle vibrations are captured using a triaxial accelerometer. The raw data is processed by a triplet network, which uses an LSTM as the base model, thereby facilitating the aggregation within classes and separation between classes. A soft-max classification layer is subsequently integrated into the model, which enables the precise determination of tool wear states. The base model is optimized using a Genetic Algorithm to ensure model efficiency and accuracy before it is expanded into a triplet network. Experimental results from a vertical machining center confirm that the triplet LSTM network offers superior accuracy compared to a standard LSTM network, even when the sample size is small.

Keywords

Tool wear monitoring triplet neural network long short-term memory network few samples Genetic Algorithm

Get full access to this article

View all access options for this article.

References

Deng

Zhang

, et al. Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. J Clean Prod 2017; 166: 1407–1414.

Chen

. On the quantitative analysis of drill edge wear when machining CFRP/Ti6Al4V stacks. Int J Adv Manuf Technol 2020; 108(5–6): 1463–1472.

Dutta

Pal

Sen

. On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression. Precis Eng 2016; 43: 34–42.

Dou

Jiao

, et al. An unsupervised online monitoring method for tool wear using a sparse auto-encoder. Int J Adv Manuf Technol 2020; 106(5–6): 2493–2507.

Zhao

Yan

Wang

, et al. Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors (Basel) 2017; 17(2): 273.

Zhang

Qiao

, et al. Analysis of feature extracting ability for cutting state monitoring using deep belief networks. Procedia CIRP 2015; 31: 29–34.

Cai

Zhang

, et al. A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 2020; 31(6): 1497–1510.

Huang

, et al. Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine. Measurement 2021; 167: 108153.

Chen

Jin

Jiri

. Predicting tool wear with multi-sensor data using deep belief networks. Int J Adv Manuf Technol 2018; 99(5–8): 1917–1926.

10.

Martínez-Arellano

Terrazas

Ratchev

. Tool wear classification using time series imaging and deep learning. Int J Adv Manuf Technol 2019; 104(9–12): 3647–3662.

11.

Zhong

Lin

. A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement 2019; 137: 435–453.

12.

Liu

, et al. Deep few-shot learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 2019; 57(4): 2290–2304.

13.

Zhang

Wang

, et al. Few-shot bearing fault diagnosis based on model-agnostic meta-learning. IEEE Trans Ind Appl 2021; 57(5): 4754–4764.

14.

Yang

Zhao

Wang

. A new intelligent bearing fault diagnosis model based on triplet network and SVM. Sci Rep 2022; 12(1): 5234.

15.

Hoffer

Ailon

. Deep metric learning using triplet network. In: International workshop on similarity-based pattern recognition, Copenhagen, Denmark, 12–14 October 2015, pp.84–92. Cham: Springer.

16.

Chai

, et al. Improved prototypical networks for few-shot learning. Pattern Recognit Lett 2020; 140: 81–87.

17.

Vinyals

Blundell

Lillicrap

, et al. Matching networks for one shot learning. In: Advances in neural information processing systems, 2016.

18.

Deihimi

Orang

Showkati

. Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 2013; 57: 382–401.

19.

Hochreiter

Schmidhuber

. Long short-term memory. Neural Comput 1997; 9(8): 1735–1780.

20.

Cai

Liu

. Maxout neurons for deep convolutional and LSTM neural networks in speech recognition. Speech Commun 2016; 77: 53–64.

21.

Holland

Reitman

. Cognitive systems based on adaptive algorithms. ACM SIGART Bull 1977; 63: 49.