These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

123 related articles for article (PubMed ID: 36554138)

  • 1. A Dual-Stage Attention Model for Tool Wear Prediction in Dry Milling Operation.
    Qin Y; Li J; Zhang C; Zhao Q; Ma X
    Entropy (Basel); 2022 Nov; 24(12):. PubMed ID: 36554138
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images.
    Yang J; Duan J; Li T; Hu C; Liang J; Shi T
    Sensors (Basel); 2022 Nov; 22(21):. PubMed ID: 36366114
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction.
    He J; Yuan L; Lei H; Wang K; Weng Y; Gao H
    Sensors (Basel); 2024 Feb; 24(4):. PubMed ID: 38400286
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion.
    Wang K; Wang A; Wu L; Xie G
    Sensors (Basel); 2024 Apr; 24(8):. PubMed ID: 38676269
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals.
    Ferrando Chacón JL; Fernández de Barrena T; García A; Sáez de Buruaga M; Badiola X; Vicente J
    Sensors (Basel); 2021 Sep; 21(17):. PubMed ID: 34502874
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230.
    Cheng M; Jiao L; Yan P; Gu H; Sun J; Qiu T; Wang X
    Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808436
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Local-feature and global-dependency based tool wear prediction using deep learning.
    Yang C; Zhou J; Li E; Wang M; Jin T
    Sci Rep; 2022 Aug; 12(1):14574. PubMed ID: 36028636
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An online monitoring method of milling cutter wear condition driven by digital twin.
    Zi X; Gao S; Xie Y
    Sci Rep; 2024 Feb; 14(1):4956. PubMed ID: 38418504
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process.
    Wu X; Liu Y; Zhou X; Mou A
    Sensors (Basel); 2019 Sep; 19(18):. PubMed ID: 31487810
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Enhancing Tool Wear Prediction Accuracy Using Walsh-Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection.
    Shah M; Borade H; Sanghavi V; Purohit A; Wankhede V; Vakharia V
    Sensors (Basel); 2023 Apr; 23(8):. PubMed ID: 37112174
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling.
    Wiciak-Pikuła M; Felusiak-Czyryca A; Twardowski P
    Sensors (Basel); 2020 Oct; 20(20):. PubMed ID: 33066308
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models.
    Sayyad S; Kumar S; Bongale A; Kotecha K; Abraham A
    Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420825
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals.
    Zhang Y; Qi X; Wang T; He Y
    Sensors (Basel); 2023 May; 23(10):. PubMed ID: 37430508
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images.
    Jang WK; Kim DW; Seo YH; Kim BH
    Sensors (Basel); 2023 Jan; 23(3):. PubMed ID: 36772248
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression.
    Rong Z; Li Y; Wu L; Zhang C; Li J
    Sensors (Basel); 2024 May; 24(11):. PubMed ID: 38894185
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning.
    Zeng S; Pi D
    Sensors (Basel); 2023 May; 23(10):. PubMed ID: 37430883
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring.
    Ou J; Li H; Huang G; Zhou Q
    Sensors (Basel); 2020 May; 20(10):. PubMed ID: 32438608
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Study of Two-Way Short- and Long-Term Memory Network Intelligent Computing IoT Model-Assisted Home Education Attention Mechanism.
    Ma S
    Comput Intell Neurosci; 2021; 2021():3587884. PubMed ID: 34970310
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.
    García Nieto PJ; García-Gonzalo E; Ordóñez Galán C; Bernardo Sánchez A
    Materials (Basel); 2016 Jan; 9(2):. PubMed ID: 28787882
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion.
    Huang PM; Lee CH
    Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450780
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.