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 *

182 related articles for article (PubMed ID: 32438608)

  • 21. Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.
    Caggiano A
    Sensors (Basel); 2018 Mar; 18(3):. PubMed ID: 29522443
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.
    Zhang C; Yao X; Zhang J; Jin H
    Sensors (Basel); 2016 May; 16(6):. PubMed ID: 27258277
    [TBL] [Abstract][Full Text] [Related]  

  • 23. 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]  

  • 24. Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.
    Wang G; Yang Y; Li Z
    Sensors (Basel); 2014 Nov; 14(11):21588-602. PubMed ID: 25405514
    [TBL] [Abstract][Full Text] [Related]  

  • 25. In-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation.
    Chen M; Mao J; Fu Y; Liu X; Zhou Y; Sun W
    Sci Rep; 2024 Jun; 14(1):12888. PubMed ID: 38839855
    [TBL] [Abstract][Full Text] [Related]  

  • 26. An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring.
    Chung TK; Yeh PC; Lee H; Lin CM; Tseng CY; Lo WT; Wang CM; Wang WC; Tu CJ; Tasi PY; Chang JW
    Sensors (Basel); 2016 Feb; 16(3):269. PubMed ID: 26907297
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Study on In-Situ Tool Wear Detection during Micro End Milling Based on Machine Vision.
    Zhang X; Yu H; Li C; Yu Z; Xu J; Li Y; Yu H
    Micromachines (Basel); 2022 Dec; 14(1):. PubMed ID: 36677161
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Tool Wear State Recognition Based on One-Dimensional Convolutional Channel Attention.
    Xue Z; Li L; Chen N; Wu W; Zou Y; Yu N
    Micromachines (Basel); 2023 Oct; 14(11):. PubMed ID: 38004840
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Simulation and Experimental Study on Reverse Helical Milling with the Gradual-Removal Reverse Edge Milling Cutter under Ultrasonic-Assisted Condition.
    Ren K; Wang G
    Materials (Basel); 2022 Jan; 15(3):. PubMed ID: 35161060
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters.
    Wang Y; Wang Y; Zheng L; Zhou J
    Sensors (Basel); 2022 Mar; 22(5):. PubMed ID: 35271142
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. A Multisensor Fusion Method for Tool Condition Monitoring in Milling.
    Zhou Y; Xue W
    Sensors (Basel); 2018 Nov; 18(11):. PubMed ID: 30423828
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Integrated Design of Spindle Speed Modulation and Cutting Vibration Suppression Controls Using Disturbance Observer for Thread Milling.
    Yeh SS; Chen CW
    Materials (Basel); 2021 Nov; 14(21):. PubMed ID: 34772183
    [TBL] [Abstract][Full Text] [Related]  

  • 34. 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]  

  • 35. Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process.
    Brili N; Ficko M; Klančnik S
    Sensors (Basel); 2021 Mar; 21(5):. PubMed ID: 33803442
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.
    Kuntoğlu M; Aslan A; Pimenov DY; Usca ÜA; Salur E; Gupta MK; Mikolajczyk T; Giasin K; Kapłonek W; Sharma S
    Sensors (Basel); 2020 Dec; 21(1):. PubMed ID: 33375340
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis.
    Turšič N; Klančnik S
    Sensors (Basel); 2024 Apr; 24(8):. PubMed ID: 38676107
    [TBL] [Abstract][Full Text] [Related]  

  • 38. An Unsupervised Condition Monitoring System for Electrode Milling Problems in the Resistance Welding Process.
    Ibáñez D; Garcia E; Soret J; Martos J
    Sensors (Basel); 2022 Jun; 22(12):. PubMed ID: 35746093
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Study on the separation effect of high-speed ultrasonic vibration cutting.
    Zhang X; Sui H; Zhang D; Jiang X
    Ultrasonics; 2018 Jul; 87():166-181. PubMed ID: 29549775
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Physics-informed Gaussian process for tool wear prediction.
    Zhu K; Huang C; Li S; Lin X
    ISA Trans; 2023 Dec; 143():548-556. PubMed ID: 37770369
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.