Letteratura scientifica selezionata sul tema "Surgical microgripper"
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Articoli di riviste sul tema "Surgical microgripper":
Dunn, Caleigh R., Bruce P. Lee e Rupak M. Rajachar. "Thermomagnetic-Responsive Self-Folding Microgrippers for Improving Minimally Invasive Surgical Techniques and Biopsies". Molecules 27, n. 16 (15 agosto 2022): 5196. http://dx.doi.org/10.3390/molecules27165196.
Yu, Lingtao, Yusheng Yan, Chenzheng Li e Xiufeng Zhang. "Three-dimensional nonlinear force-sensing method based on double microgrippers with E-type vertical elastomer for minimally invasive robotic surgery". Robotica 36, n. 6 (30 gennaio 2018): 865–81. http://dx.doi.org/10.1017/s0263574718000085.
Vurchio, Federica, Pietro Ursi, Francesco Orsini, Andrea Scorza, Rocco Crescenzi, Salvatore A. Sciuto e Nicola P. Belfiore. "Toward Operations in a Surgical Scenario: Characterization of a Microgripper via Light Microscopy Approach". Applied Sciences 9, n. 9 (9 maggio 2019): 1901. http://dx.doi.org/10.3390/app9091901.
Aubeeluck, D. Anastasia, Cameron Forbrigger, Sara Mohseni Taromsari, Tianhao Chen, Eric Diller e Hani E. Naguib. "Screen-Printed Resistive Tactile Sensor for Monitoring Tissue Interaction Forces on a Surgical Magnetic Microgripper". ACS Applied Materials & Interfaces, 5 luglio 2023. http://dx.doi.org/10.1021/acsami.3c04821.
Pasaguayo, Liseth, Zeina AL Masry, Sergio Lescano e Noureddine Zerhouni. "Surgical Microgrippers: A Survey And Analysis". Journal of Medical Devices, 11 luglio 2023, 1–47. http://dx.doi.org/10.1115/1.4062950.
Zhou, Huaijuan, Shengchang Zhang, Zijian Liu, Bowen Chi, Jinhua Li e Yilong Wang. "Untethered Microgrippers for Precision Medicine". Small, 8 novembre 2023. http://dx.doi.org/10.1002/smll.202305805.
Tesi sul tema "Surgical microgripper":
Pasaguayo, Baez Liseth Victoria. "Degradation modeling and analysis for a microgripper for intracorporeal surgery". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD007.
This research work deals with the degradation modeling and analysis for a microgripper for intracorporeal surgery. We first conducted a literature review to identify limitations for Prognostics and Health Management (PHM) implementation in medical microsystems. Secondly, a methodology based on risk management according to ISO 14971 for medical devices was developed to select the critical components of the microgripper. Thirdly, the data was collected on the microgripper system's kinematics, considering the angular position, velocity, acceleration, and jerk variables through a methodology that included data requirements, methods, and protocols. Once data were available, data analysis was performed, which allowed an understanding of the degradation behavior of the microgripper system, this understanding led to the identification of three distinct stages of degradation, which were categorized into three zones: safety, degradation, and critical. Moreover, it was identified the larger the closing range, the lower the number of cycles before failure occurs. Lastly, to predict the remaining useful life (RUL) of the microgripper system, a machine learning and deep learning approach was implemented. This approach consisted of fusing Gradient Boosting and Long short-term memory (LSTM) results to predict the RUL. The proposed approach performance was validated by the results of the RMSE, MAE, and R^2 metrics, as well as the online RUL implementation