Skip to main content
SpringerLink
Log in

Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Deep learning-based data-driven methods have been successfully developed in tool wear condition monitoring (TWCM), relying on the massive available labeled samples and the same probability distribution between training and testing data. However, these two prerequisites are often difficult to satisfy in actual industries, which results in significant performance deterioration of those methods. This paper proposes an intelligent cross-domain data-driven TWCM method based on feature transfer by a deep adversarial domain confusion network (DADCN) model. In this model, source and target feature extractors sharing the same network architecture are employed to obtain high-level representation from time–frequency spectrums of vibration signals in the different domains respectively. An independent adversarial learning mechanism is designed in domain obfuscator to learn domain-invariant feature knowledge, while the maximum mean discrepancy is applied to measure the distribution difference between different domains. A cross-domain classifier is utilized for tool wear condition monitoring across machining processes. The performances of the proposed DADCN model under two distribution measure criteria are experimentally demonstrated using six transfer tasks between laboratory and factory platforms. The results indicate that the DADCN model can improve the monitoring accuracy and exhibit distinct clustering of tool wear conditions, promoting a successful application of data-driven methods in actual industrial fields.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Abbreviations

BNM:

Batch nuclear-norm maximization

BNMN:

Batch nuclear-norm maximization network

CNN:

Convolutional neural network

CNN-LSTM:

Convolutional neural network-long short-term memory

CORAL:

Correlation alignment

CPU:

Central processing unit

CRL:

Condition recognition loss

CUDA:

Compute unified device architecture

DADCN:

Deep adversarial domain confusion network

DAN:

Deep adaptation network

DANN:

Domain adversarial neural network

DBN:

Deep belief network

DCAN:

Deep correlation alignment network

DCL:

Domain confusion loss

DCNN:

Deep convolutional neural network

DDC:

Deep domain confusion

DIL:

Domain identification loss

DML:

Distribution measure loss

DRN:

Deep residual network

DTL:

Deep transfer learning

FC:

Fully-connected

GPU:

Graphics processing unit

GRL:

Gradient reversal layer

LSTM:

Long short-term memory

MK-MMD:

Multiple kernel maximum mean discrepancy

MMD:

Maximum mean discrepancy

ReLU:

Rectified Linear Unit

RKHS:

Reproducible kernel Hilbert space

RUL:

Remaining useful life

SOP:

Standard operation procedure

SSAE:

Stacked sparse auto-encoders

STFT:

Short-time Fourier transform

TWCM:

Tool wear condition monitoring

\(\alpha\) :

Penalty coefficient for \(L_{g}\)

\(\beta\) :

Penalty coefficient for \(L_{m}\)

\(\varepsilon\) :

Adjustment factor of dynamical learning rate

\(\eta\) :

Dynamical learning rate

\(\eta_{0}\) :

Initial learning rate

\(\theta_{C}\) \(\theta_{D}\), \(\theta_{{F_{S} }}\), and \(\theta_{{F_{T} }}\) :

Training parameters of \(C\), \(D\), \(F_{S}\), and \(F_{T}\)

\(\theta_{C}^{ * }\), \(\theta_{D}^{ * }\), \(\theta_{{F_{S} }}^{ * }\), and \(\theta_{{F_{T} }}^{ * }\) :

Optimal values of \(\theta_{C}\), \(\theta_{D}\), \(\theta_{{F_{S} }}\), and \(\theta_{{F_{T} }}\)

\(\theta_{{C_{a} }}\), \(\theta_{{D_{a} }}\), and \(\theta_{{F_{a} }}\) :

Training parameters of \(C_{a}\), \(D_{a}\), and \(F_{a}\)

\(\theta_{{C_{a} }}^{*}\), \(\theta_{{D_{a} }}^{*}\), and \(\theta_{{F_{a} }}^{*}\) :

Optimal values of \(\theta_{{C_{a} }}\), \(\theta_{{D_{a} }}\), and \(\theta_{{F_{a} }}\)

\({{\varvec{\uptheta}}}_{i}^{l}\) :

Parameter set to be trained of the l-th residual unit

\(\mu\) :

Adjustment factor of dynamical learning rate

\(\sigma_{R} \left( \times \right)\) :

Activation function of rectified linear unit

\(\Upsilon^{S}\), \(\Upsilon^{T}\) :

Label space in source and target domains

\(\varphi_{u}\) :

Parameter of convex optimization

\(\chi^{S}\), \(\chi^{T}\) :

Feature space in source and target domains

\({\mathbf{w}}_{d}^{{F_{3}^{D} }}\) :

The weight adjacent to the layer \(F_{3}^{D}\)

\({\mathbf{w}}_{k}^{o}\) :

The weight adjacent to the output layer

\(C\) :

Cross-domain classifier of the DADCN model

\(C_{a}\) :

Classifier of adversarial domain adaptation

\(D\) :

Domain obfuscator of the DADCN model

\(D_{a}\) :

Domain discriminator of adversarial domain adaptation

\(\mathcal{D}_{S}\), \(\mathcal{D}_{T}\) :

Source and target domains

\(F_{1}^{C}\) :

First fully-connected layer in the cross-domain classifier \(C\)

\(F_{1}^{D}\) :

First fully-connected layer in the domain obfuscator \(D\)

\(F_{2}^{C}\) :

Second fully-connected layer in the cross-domain classifier \(C\)

\(F_{2}^{D}\) :

Second fully-connected layer in the domain obfuscator \(D\)

\(F_{3}^{C}\) :

Output layer in the cross-domain classifier \(C\)

\(F_{3}^{D}\) :

Output layer in the domain obfuscator \(D\)

\(F_{a}\) :

Feature extractor of adversarial domain adaptation

\(F_{S}\), \(F_{T}\) :

Source and target feature extractor of the DADCN model

\({\text{F}}\left( {\varvec{x}_{i}^{lI} ;\varvec\theta_{i}^{l} } \right)\) :

The residual function of the l-th residual block

\(g\left( { \cdot , \cdot } \right)\) :

Gaussian kernel function

\(H\) :

Reproducible kernel Hilbert space

\(h_{o} \left( {{\mathbf{x}}_{i} } \right)\) :

The feature representation of the i-th input sample \({\mathbf{x}}_{i}\)

\(L_{c}\) :

Condition recognition loss

\(L_{d}\) :

Domain identification loss

\(L_{g}\) :

Domain confusion loss

\(L_{m}\) :

Distribution measure loss

\(k_{s}\), \(k_{t}\) :

Condition number of tool wear in source and target domains

\(n_{s}\), \(n_{t}\) :

Sample number in source and target domains

\({\mathbf{x}}_{i}\) :

The i-th input sample

\({\mathbf{x}}_{i}^{s}\), \({\mathbf{x}}_{i}^{t}\) :

The i-th feature matrix in source and target domains

\({\mathbf{x}}^{R}\) :

The R-th level feature vector

\(x_{j,n}^{R}\) :

Element in the j-th row and n-th column of the R-th level feature vector \({\mathbf{x}}^{R}\)

\(\varvec{x}_{i}^{lI}\) :

The i-th input of the l-th residual block

\({\mathbf{x}}_{i}^{F}\) :

The i-th input feature representation \({\mathbf{x}}_{i}^{F}\) in domain obfuscator

\({\mathbf{x}}_{C,i,j}^{s,o}\) :

Network output vector of the last layer in the cross-domain classifier \(C\), taking the i-th source domain sample as input

\({\mathbf{x}}_{D,i,d}^{s,o}\), \({\mathbf{x}}_{D,i,d}^{t,o}\) :

Network output vector of the last layer in the domain obfuscator \(D\), taking the i-th source and target domains sample as input respectively

\(\varvec{y}_{i}^{lI}\) :

The i-th output of the l-th residual block

\(y_{i}^{s}\) :

The corresponding label of \({\mathbf{x}}_{C,i,j}^{s,o}\)

References

  • Alonso, F. J., & Salgado, D. R. (2008). Analysis of the structure of vibration signals for tool wear detection. Mechanical Systems and Signal Processing, 22(3), 735–748.

    Article  ADS  Google Scholar 

  • Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., & Deliou, A. (2017). A new time–frequency method for identification and classification of ball bearing faults. Journal of Sound and Vibration, 397, 241–265.

    Article  ADS  Google Scholar 

  • Babu, M. S., & Rao, T. B. (2022). Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM. Measurement, 199, 111501. https://doi.org/10.1016/j.measurement.2022.111501

    Article  Google Scholar 

  • Bagr, S., Manwar, A., Varghese, A., Mujumdar, S., & Joshi, S. S. (2021). Tool wear and remaining useful life prediction in micro-milling along complex tool paths using neural networks. Journal of Manufacturing Processes, 71, 679–698.

    Article  Google Scholar 

  • Bajaj, N. S., Patange, A. D., Jegadeeshwaran, R., Kulkarni, K. A., Ghatpande, R. S., & Kapadnis, A. M. (2022). A bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets. Journal of Nondestructive Evaluation, Diagnostics & Prognostics of Engineering Systems, 5(2), 021002. https://doi.org/10.1115/1.4051696

    Article  Google Scholar 

  • Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.

    Article  MathSciNet  Google Scholar 

  • Bustillo, A., Urbikain, G., Perez, J. M., Pereira, O. M., & López de Lacalle, L. N. (2018). Smart optimization of a friction-drilling process based on boosting ensembles. Journal of Manufacturing Systems, 48, 108–121.

    Article  Google Scholar 

  • Cai, W., Zhang, W., Hu, X., & Liu, Y. (2020). A hybrid information model based on long short-term memory network for tool condition monitoring. Journal of Intelligent Manufacturing, 31(6), 1497–1510.

    Article  Google Scholar 

  • Chen, Z., He, G., Li, J., Liao, Y., Gryllias, K., & Li, W. (2020). Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery. IEEE Transactions on Instrumentation and Measurement, 69(11), 8702–8712.

    Article  ADS  Google Scholar 

  • Cheng, C., Zhou, B., Ma, G., Wu, D., & Yuan, Y. (2020). Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data. Neurocomputing, 409, 35–45.

    Article  Google Scholar 

  • Cheng, H., Kong, X., Wang, Q., Ma, H., Yang, S., & Chen, G. (2021). Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01814-y

    Article  Google Scholar 

  • Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., & Tian, Q. (2020). Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3941–3950).

  • Deebak, B. D., & Al-Turjman, F. (2021). Digital-twin assisted: Fault diagnosis using deep transfer learning for machining tool condition. International Journal of Intelligent Systems. https://doi.org/10.1002/int.22493

    Article  Google Scholar 

  • Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1), 2096–2030.

    MathSciNet  Google Scholar 

  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems (pp. 2672–2680). Curran Associates.

    Google Scholar 

  • Goyal, D., Mongia, C., & Sehgal, S. (2021). Applications of digital signal processing in monitoring machining processes and rotary components: A review. IEEE Sensors Journal, 21(7), 8780–8804.

    Article  ADS  Google Scholar 

  • Gretton, A., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K., & Sriperumbudur, B. K. (2012). Optimal kernel choice for large-scale two-sample tests. Advances in Neural Information Processing Systems, 25, 1205–1213.

    Google Scholar 

  • Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2019). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 66(9), 7316–7325.

    Article  Google Scholar 

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90

  • Hsieh, W. H., Lu, M. C., & Chiou, S. J. (2012). Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. The International Journal of Advanced Manufacturing Technology, 61(1–4), 53–61.

    Article  Google Scholar 

  • Huang, Z., Zhu, J., Lei, J., Li, X., & Tian, F. (2020). Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31(4), 953–966.

    Article  Google Scholar 

  • Huang, Z., Zhu, J., Lei, J., Li, X., & Tian, F. (2021). Tool wear monitoring with vibration signals based on short-time Fourier transform and deep convolutional neural network in milling. Mathematical Problems in Engineering, 2021, 9976939.

    Article  Google Scholar 

  • Javed, K., Gouriveau, R., Li, X., & Zerhouni, N. (2018). Tool wear monitoring and prognostics challenges: A comparison of connectionist methods toward an adaptive ensemble model. Journal of Intelligent Manufacturing, 29(8), 1873–1890.

    Article  Google Scholar 

  • Kouw, W. M., & Loog, M. (2021). A review of domain adaptation without target labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 766–785.

    Article  Google Scholar 

  • Lee, J. Y. (2015). Variable short-time Fourier transform for vibration signals with transients. Journal of Vibration and Control, 21(7), 1383–1397.

    Article  Google Scholar 

  • Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834.

    Article  ADS  Google Scholar 

  • Li, X., Lim, B. S., Zhou, J. H., Huang, S., Phua, S. J., Shaw, K. C., & Er, M. J. (2009). Fuzzy neural network modelling for tool wear estimation in dry milling operation. In Annual Conference of the prognostics and health management Society (pp. 1–11).

  • Li, C., Zhang, S., Qin, Y., & Estupinan, E. (2020a). A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, 407, 121–135.

    Article  Google Scholar 

  • Li, J., Lu, J., Chen, C., Ma, J., & Liao, X. (2021). Tool wear state prediction based on feature-based transfer learning. The International Journal of Advanced Manufacturing Technology, 113(11–12), 3283–3301.

    Article  Google Scholar 

  • Li, X., Zhang, W., Xu, N. X., & Ding, Q. (2020b). Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places. IEEE Transactions on Industrial Electronics, 67(8), 6785–6794.

    Article  Google Scholar 

  • Liu, X., Liu, S., Li, X., Zhang, B., Yue, C., & Liang, S. Y. (2021). Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network. Journal of Manufacturing Systems, 60, 608–619.

    Article  Google Scholar 

  • Long, M., Cao, Y., Cao, Z., Wang, J., & Jordan, M. I. (2019). Transferable representation learning with deep adaptation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12), 3071–3085.

    Article  PubMed  Google Scholar 

  • López de Lacalle, L. N., Lamikiz, A., Sánchez, J. A., & Fernández de Bustos, I. (2005). Simultaneous measurement of forces and machine tool position for diagnostic of machining tests. IEEE Transactions on Instrumentation and Measurement, 54(6), 2329–2335.

    Article  ADS  Google Scholar 

  • López de Lacalle, L. N., Lamikiz, A., Sánchez, J. A., & Fernández de Bustos, I. (2006). Recording of real cutting forces along the milling of complex parts. Mechatronics, 16(1), 21–32.

    Article  Google Scholar 

  • Marani, M., Zeinali, M., Songmene, V., & Mechefske, C. K. (2021). Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling. Measurement, 177, 109329. https://doi.org/10.1016/j.measurement.2021.109329

    Article  Google Scholar 

  • Marei, M., El Zaatari, S., & Li, W. (2021). Transfer learning enabled convolutional neural networks for estimating health state of cutting tools. Robotics and Computer-Integrated Manufacturing, 71, 102145. https://doi.org/10.1016/j.rcim.2021.102145

    Article  Google Scholar 

  • Marei, M., & Li, W. (2021). Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-021-07784-y

    Article  Google Scholar 

  • Martinez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104(9), 3647–3662.

    Article  Google Scholar 

  • Nasir, V., & Sassani, F. (2021). A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges. The International Journal of Advanced Manufacturing Technology, 115(9–10), 2683–2709.

    Article  Google Scholar 

  • Ou, J., Li, H., Huang, G., Liu, B., & Wang, Z. (2021). Tool wear recognition based on deep kernel autoencoder with multichannel signals fusion. IEEE Transactions on Instrumentation and Measurement, 20, 3521909. https://doi.org/10.1109/TIM.2021.3096283

    Article  Google Scholar 

  • Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 199–210.

    Article  PubMed  Google Scholar 

  • Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  • Rehorn, A. G., Jiang, J., & Orban, P. E. (2005). State-of-the-art methods and results in tool condition monitoring: A review. The International Journal of Advanced Manufacturing Technology, 26(7–8), 693–710.

    Article  Google Scholar 

  • Rivero, A. D., López de Lacalle, L. N., & Penalva, M. L. (2008). Tool wear detection in dry high-speed milling based upon the analysis of machine internal signals. Mechatronics, 18(10), 627–633.

    Article  Google Scholar 

  • Serin, G., Sener, B., Ozbayoglu, A. M., & Unver, H. O. (2020). Review of tool condition monitoring in machining and opportunities for deep learning. International Journal of Advanced Manufacturing Technology, 109(3–4), 953–974.

    Article  Google Scholar 

  • Shi, C., Panoutsos, G., Luo, B., Liu, H., Li, B., & Lin, X. (2019). Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprec’s on manufacturing. IEEE Transactions on Industrial Electronics, 66(5), 3794–3803.

    Article  Google Scholar 

  • Silva, R., & Araújo, A. (2020). A novel approach to condition monitoring of the cutting process using recurrent neural networks. Sensors, 20(16), 4493.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Sun, B., & Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. European conference on computer vision (pp. 443–450). International Publishing.

    Google Scholar 

  • Sun, C., Ma, M., Zhao, Z., Tian, S., Yan, R., & Chen, X. (2019). Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing. IEEE Transactions on Industrial Informatics, 15(4), 2416–2425.

    Article  Google Scholar 

  • Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474.

  • Van Der Maaten, L. (2014). Accelerating t-SNE using tree-based algorithms. The Journal of Machine Learning Research, 15(1), 3221–3245.

    MathSciNet  Google Scholar 

  • Wang, Y., Qin, B., Liu, K., Shen, M., & Han, L. (2021). A new multi-task learning method for tool wear condition and part surface quality prediction. IEEE Transactions on Industrial Informatics, 17(9), 6023–6033.

    Article  Google Scholar 

  • Wong, S. Y., Chuah, J. H., & Yap, H. J. (2020). Technical data-driven tool condition monitoring challenges for CNC milling: A review. International Journal of Advanced Manufacturing Technology, 107(11–12), 4837–4857.

    Article  Google Scholar 

  • Wu, L., Li, C., Chen, Q., & Li, B. (2020). Deep adversarial domain adaptation network. International Journal of Advanced Robotic Systems, 17(5), 1729881420964648. https://doi.org/10.1177/1729881420964648

    Article  Google Scholar 

  • Yan, B., Zhu, L., & Dun, Y. (2021). Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning. Journal of Manufacturing Systems, 61, 495–508.

    Article  Google Scholar 

  • Yu, C., Wang, J., Chen, Y., & Huang, M. (2019). Transfer learning with dynamic adversarial adaptation network. In 2019 IEEE International Conference on Data Mining (pp. 778–786).

  • Zhao, B., Zhang, X. M., Zhan, Z. H., & Wu, Q. Q. (2021). Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis. Journal of Manufacturing Systems, 59, 565–576.

    Article  Google Scholar 

  • Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.

    Article  ADS  Google Scholar 

  • Zhou, Y., Sun, B., Sun, W., & Lei, Z. (2020). Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01663-1

    Article  Google Scholar 

  • Zhu, Y., Zhuang, F., Wang, J., Chen, J., Shi, Z., Wu, W., & He, Q. (2019). Multi-representation adaptation network for cross-domain image classification. Neural Networks, 119, 214–221.

    Article  PubMed  Google Scholar 

  • Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., & He, Q. (2021). Deep subdomain adaptation network for image classification. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1713–1722.

    Article  ADS  MathSciNet  PubMed  Google Scholar 

Download references

Acknowledgements

This work was financially supported in part by the National Natural Science Foundation of China (Grant No. 51775323), and the Interdisciplinary Program of the University of Shanghai for Science and Technology (No.10-20-304-402). Our deepest gratitude goes to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Zhiwen Huang, Jianmin Zhu, Wei Zhang & Xiaoru Li

  2. School of Mechanical Engineering, Tongji University, Shanghai, 200092, China

    Jiajie Shao

  3. Public Experiment Center, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Wei Zhang

Authors
  1. Zhiwen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiajie Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianmin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoru Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianmin Zhu.

Ethics declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Z., Shao, J., Zhu, J. et al. Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network. J Intell Manuf 35, 1079–1105 (2024). https://doi.org/10.1007/s10845-023-02088-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-023-02088-2

Keywords

Search

Navigation