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Journal articles on the topic 'Force detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kageshima, Masami, Hisato Ogiso, Shizuka Nakano, Mark A. Lantz, and Hiroshi Tokumoto. "Atomic Force Microscopy Cantilevers for Sensitive Lateral Force Detection." Japanese Journal of Applied Physics 38, Part 1, No. 6B (1999): 3958–61. http://dx.doi.org/10.1143/jjap.38.3958.

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2

Goldmann, Tomas, and Lucie Himmlova. "EXPERIMENTAL DETECTION OF CHEWING FORCE." Journal of Biomechanics 41 (July 2008): S341. http://dx.doi.org/10.1016/s0021-9290(08)70340-3.

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3

Rugar, D., B. C. Stipe, H. J. Mamin, et al. "Adventures in attonewton force detection." Applied Physics A 72, S1 (2001): S3—S10. http://dx.doi.org/10.1007/s003390100729.

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4

Fermani, R., S. Mancini, and P. Tombesi. "Entanglement assisted weak force detection." Fortschritte der Physik 52, no. 11-12 (2004): 1110–17. http://dx.doi.org/10.1002/prop.200410181.

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5

Narayan, Meenakshi, Michael A. Choti, and Ann Majewicz Fey. "Data-Driven Detection of Needle Buckling Events in Robotic Needle Steering." Journal of Medical Robotics Research 04, no. 02 (2019): 1850005. http://dx.doi.org/10.1142/s2424905x18500058.

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In robotic needle steering, flexible asymmetric-tip needles can steer around obstacles to reach targets deep within tissue. Due to tissue inhomogeneity and needle flexibility, needle buckling can occur, preventing accurate placement. This paper focuses on detecting needle buckling using axial force and needle-tip position readings from sensors. Our algorithm uses errors between the force readings and a predictive force model generated from those readings to track rapid changes in the measured forces. Using this prediction error and needle-tip position, the algorithm detects unexpected force in

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6

Zheng, Dong Xi. "The Measurement of Cutting-Force during Compensating the Errors of NC Process." Advanced Materials Research 756-759 (September 2013): 560–63. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.560.

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Cutting-force error is the main portion of errors in the NC process, and the analysis of the cutting-force error is based on the measurement of cutting force. Introduced the detection method of cutting force, and analyzed the principle of cutting-force detection by detecting the current of servo motor.

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7

Ito, So, Yusuke Shima, Daichi Kato, Kimihisa Matsumoto, and Kazuhide Kamiya. "Development of a Microprobing System for Side Wall Detection Based on Local Surface Interaction Force Detection." International Journal of Automation Technology 14, no. 1 (2020): 91–98. http://dx.doi.org/10.20965/ijat.2020.p0091.

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This study proposes a novel microprobing system for the surface detection of the side wall of micrometric scale workpieces based on the detection of the local surface interaction force. A spherical tip-shaped glass capillary tube with a micrometric scale diameter was employed as a micro-stylus. To obtain a low measuring force, the local attractive interaction force on the surface of the workpieces was detected by the vibrating micro-stylus and used as the probing trigger signal. The vibration in the main axis direction of the stylus allowed detection of the local surface interaction force in a

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8

Lee, Seungjun, Seong Min Roh, Eunji Lee, et al. "Applications of Converged Various Forces for Detection of Biomolecules and Novelty of Dielectrophoretic Force in the Applications." Sensors 20, no. 11 (2020): 3242. http://dx.doi.org/10.3390/s20113242.

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Since separation of target biomolecules is a crucial step for highly sensitive and selective detection of biomolecules, hence, various technologies have been applied to separate biomolecules, such as deoxyribonucleic acid (DNA), protein, exosome, virus, etc. Among the various technologies, dielectrophoresis (DEP) has the significant advantage that the force can provide two different types of forces, attractive and repulsive DEP force, through simple adjustment in frequency or structure of microfluidic chips. Therefore, in this review, we focused on separation technologies based on DEP force an

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9

Kohlgraf-Owens, Dana C., Sergey Sukhov, Léo Greusard, Yannick De Wilde, and Aristide Dogariu. "Optically induced forces in scanning probe microscopy." Nanophotonics 3, no. 1-2 (2014): 105–16. http://dx.doi.org/10.1515/nanoph-2013-0056.

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AbstractTypical measurements of light in the near-field utilize a photodetector such as a photomultiplier tube or a photodiode, which is placed remotely from the region under test. This kind of detection has many draw-backs including the necessity to detect light in the far-field, the influence of background propagating radiation, the relatively narrowband operation of photodetectors which complicates the operation over a wide wavelength range, and the difficulty in detecting radiation in the far-IR and THz. Here we review an alternative near-field light measurement technique based on the dete

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10

Yokoyama, Hiroshi, Takahito Inoue, and Junji Itoh. "Nonresonant detection of electric force gradients by dynamic force microscopy." Applied Physics Letters 65, no. 24 (1994): 3143–45. http://dx.doi.org/10.1063/1.112462.

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11

Kobayashi, Naritaka, Yan Jun Li, Yoshitaka Naitoh, Masami Kageshima, and Yasuhiro Sugawara. "High-Sensitivity Force Detection by Phase-Modulation Atomic Force Microscopy." Japanese Journal of Applied Physics 45, No. 30 (2006): L793—L795. http://dx.doi.org/10.1143/jjap.45.l793.

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12

Yousefizadeh, Shirin, and Thomas Bak. "Unknown External Force Estimation and Collision Detection for a Cooperative Robot." Robotica 38, no. 9 (2019): 1665–81. http://dx.doi.org/10.1017/s0263574719001681.

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SUMMARYIn human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to com

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13

Wago, K. "Low-temperature magnetic resonance force detection." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 14, no. 2 (1996): 1197. http://dx.doi.org/10.1116/1.588513.

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14

Göddenhenrich, T., H. Lemke, U. Hartmann, and C. Heiden. "Force microscope with capacitive displacement detection." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 8, no. 1 (1990): 383–87. http://dx.doi.org/10.1116/1.576401.

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15

ISHIKAWA, Makoto, Ryuichi HARADA, Naruo SASAKI, and Kouji MIURA. "SEM with a Force Detection Manipulator." Hyomen Kagaku 29, no. 11 (2008): 713–15. http://dx.doi.org/10.1380/jsssj.29.713.

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16

Rugar, D., O. Zuger, S. Hoen, C. S. Yannoni, H. M. Vieth, and R. D. Kendrick. "Force Detection of Nuclear Magnetic Resonance." Science 264, no. 5165 (1994): 1560–63. http://dx.doi.org/10.1126/science.264.5165.1560.

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17

Schemmel, A., and H. E. Gaub. "Single molecule force spectrometer with magnetic force control and inductive detection." Review of Scientific Instruments 70, no. 2 (1999): 1313–17. http://dx.doi.org/10.1063/1.1149590.

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18

Ziegler, Dominik, and Andreas Stemmer. "Force gradient sensitive detection in lift-mode Kelvin probe force microscopy." Nanotechnology 22, no. 7 (2011): 075501. http://dx.doi.org/10.1088/0957-4484/22/7/075501.

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19

Watanabe, S. "Two-directional dynamic mode force microscopy: Detection of directional force gradient." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 12, no. 3 (1994): 1577. http://dx.doi.org/10.1116/1.587290.

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20

Yang, Liuqing, Richard E. DeVor, and Shiv G. Kapoor. "Analysis of Force Shape Characteristics and Detection of Depth-of-Cut Variations in End Milling." Journal of Manufacturing Science and Engineering 127, no. 3 (2004): 454–62. http://dx.doi.org/10.1115/1.1947207.

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This paper proposes an analytical approach to detect depth-of-cut variations based on the cutting-force shape characteristics in end milling. Cutting forces of a single-flute end mill are analyzed and classified into three types according to their shape characteristics. Cutting forces of a multiple-flute end mill are then classified by considering both the cutting types of the corresponding single-flute end mill and the degree of overlap of successive flutes in the cut. Force indices are extracted from the cutting forces and depth-of-cut variations are detected based on the changes of the forc

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21

Anderson, Mark. "The Detection of Long-Chain Bio-Markers Using Atomic Force Microscopy." Applied Sciences 9, no. 7 (2019): 1280. http://dx.doi.org/10.3390/app9071280.

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The detection of long-chain biomolecules on mineral surfaces is presented using an atomic force microscope (AFM). This is achieved by using the AFM’s ability to manipulate molecules and measure forces at the pico-newton scale. We show that a highly characteristic force-distance signal is produced when the AFM tip is used to detach long-chain molecules from a surface. This AFM force spectroscopy method is demonstrated on bio-films, spores, fossils and mineral surfaces. The method works with AFM imaging and correlated tip enhanced infrared spectroscopy. The use of AFM force spectroscopy to detec

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22

Jung, Kihyo, Byung Hwa Lee, Sang Won Seo, et al. "Quantitative Assessment Method of Force Tracking Capabilities for Detection of Motor Intentional Disorders." Applied Sciences 11, no. 7 (2021): 3244. http://dx.doi.org/10.3390/app11073244.

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Early detection of motor intentional disorders associated with dysfunction in the action–intention system of the brain is clinically important to provide timely intervention. This study developed a force tracking system that can record forces exerted by the index finger while tracking 5 N, 10 N, 15 N, and 20 N of target forces varying over time. The force tracking system quantified force control measures (initiation time IT; development time, DT, maintenance error, ME; termination time, TT; tracking error, TE) for the individual and overall force control phases. This study evaluated the effect

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23

Hashimura, Shinji, Hisanori Sakai, Kai Kubota, Nozomi Ohmi, Takefumi Otsu, and Kyoichi Komatsu. "Influence of Configuration Error in Bolted Joints on Detection Error of Clamp Force Detection Method." International Journal of Automation Technology 15, no. 4 (2021): 396–403. http://dx.doi.org/10.20965/ijat.2021.p0396.

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Clamp force errors in bolted joints often cause accidents in various mechanical structures. Therefore, the clamp force must be controlled accurately and maintained for securing the reliability of mechanical structures such as vehicles. However, the clamp force cannot be controlled easily during tightening. Moreover, it is difficult to detect the clamp force after tightening. We previously proposed a method to easily detect the clamp force of a bolted joint that has been tightened. In that method, the bolt thread protruding from the nut is pulled while the nut’s upper surface is supported. The

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24

Andre, Guillaume, Kees Leenhouts, Pascal Hols, and Yves F. Dufrêne. "Detection and Localization of Single LysM-Peptidoglycan Interactions." Journal of Bacteriology 190, no. 21 (2008): 7079–86. http://dx.doi.org/10.1128/jb.00519-08.

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ABSTRACT The lysin motif (LysM) is a ubiquitous protein module that binds peptidoglycan and structurally related molecules. Here, we used single-molecule force spectroscopy (SMFS) to measure and localize individual LysM-peptidoglycan interactions on both model and cellular surfaces. LysM modules of the major autolysin AcmA of Lactococcus lactis were bound to gold-coated atomic force microscopy tips, while peptidoglycan was covalently attached onto model supports. Multiple force curves recorded between the LysM tips and peptidoglycan surfaces yielded a bimodal distribution of binding forces, pr

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25

Collins, L., M. B. Okatan, Q. Li, et al. "Quantitative 3D-KPFM imaging with simultaneous electrostatic force and force gradient detection." Nanotechnology 26, no. 17 (2015): 175707. http://dx.doi.org/10.1088/0957-4484/26/17/175707.

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26

García-Massó, Xavier, Matthias C. Huber, Jacqueline Friedmann, Luis M. Gonzalez, Stefan M. Schiller, and José L. Toca-Herrera. "Automated detection of protein unfolding events in atomic force microscopy force curves." Microscopy Research and Technique 79, no. 11 (2016): 1105–11. http://dx.doi.org/10.1002/jemt.22764.

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27

Nie, H. Y., W. Mizutani, and H. Tokumoto. "Au(111) reconstruction observed by atomic force microscopy with lateral force detection." Surface Science 311, no. 1-2 (1994): L649—L654. http://dx.doi.org/10.1016/0039-6028(94)90467-7.

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28

Hong, D. K., S. A. Han, J. H. Park, Soon Huat Tan, Naesung Lee, and Yongho Seo. "Frictional force detection from lateral force microscopic image using a Si grating." Colloids and Surfaces A: Physicochemical and Engineering Aspects 313-314 (February 2008): 567–70. http://dx.doi.org/10.1016/j.colsurfa.2007.04.161.

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29

Kamezaki, Mitsuhiro, Hiroyasu Iwata, and Shigeki Sugano. "Identification of Dominant Error Force Component in Hydraulic Pressure Reading for External Force Detection in Construction Manipulator." Journal of Robotics and Mechatronics 24, no. 1 (2012): 95–104. http://dx.doi.org/10.20965/jrm.2012.p0095.

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The purpose of this paper is to develop a fundamental external-force-detection framework for construction manipulators. Such an industrial application demands the practicality that satisfies detection requirements such as the accuracy and robustness while ensuring (i) a low cost, (ii) wide applicability, and (iii) a simple detection algorithm. For satisfying (i) and (ii), our framework first adopts a hydraulic sensor as a force sensor. However, hydraulic-pressure readings essentially include error force components. These components depend strongly on the joint kinetic state and differ in the i

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30

Hancock, Bill. "New IETF task force on intrusion detection." Computers & Security 18, no. 2 (1999): 96. http://dx.doi.org/10.1016/s0167-4048(99)90025-1.

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31

Volpe, Giovanni, Gregory Kozyreff, and Dmitri Petrov. "Backscattering position detection for photonic force microscopy." Journal of Applied Physics 102, no. 8 (2007): 084701. http://dx.doi.org/10.1063/1.2799047.

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32

Mamin, H. J., and D. Rugar. "Sub-attonewton force detection at millikelvin temperatures." Applied Physics Letters 79, no. 20 (2001): 3358–60. http://dx.doi.org/10.1063/1.1418256.

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33

Budakian, Raffi, and Seth J. Putterman. "Force detection using a fiber-optic cantilever." Applied Physics Letters 81, no. 11 (2002): 2100–2102. http://dx.doi.org/10.1063/1.1503874.

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34

Park, Ji Su, Chang Min Lee, Sang-Mo Koo, and Choong Hyun Kim. "Gait Phase Detection Using Force Sensing Resistors." IEEE Sensors Journal 20, no. 12 (2020): 6516–23. http://dx.doi.org/10.1109/jsen.2020.2975790.

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35

Stowe, T. D., K. Yasumura, T. W. Kenny, D. Botkin, K. Wago, and D. Rugar. "Attonewton force detection using ultrathin silicon cantilevers." Applied Physics Letters 71, no. 2 (1997): 288–90. http://dx.doi.org/10.1063/1.119522.

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36

Proksch, Roger, and E. Dan Dahlberg. "A detection technique for scanning force microscopy." Review of Scientific Instruments 64, no. 4 (1993): 912–16. http://dx.doi.org/10.1063/1.1144143.

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37

Ito, So, Hirotaka Kikuchi, Yuan-Liu Chen, Yuki Shimizu, and Wei Gao. "1511 Micro-probing system for slit width measurement by using a shear-force detection." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1511–1_—_1511–4_. http://dx.doi.org/10.1299/jsmelem.2015.8._1511-1_.

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38

Honda, Satomi, Yuki Unno, Koji Maruhashi, Masahiko Takenaka, and Satoru Torii. "Detection of Novel-Type Brute Force Attacks Used Ephemeral Springboard IPs as CamouflageDetection of Novel-Type Brute Force Attacks Used Ephemeral Springboard IPs as Camouflage." Journal of Advances in Computer Networks 2, no. 4 (2014): 279–86. http://dx.doi.org/10.7763/jacn.2014.v2.126.

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39

Zuo, Guoyu, Yongkang Qiu, and Yuelei Liu. "Sensorless External Force Detection Method for Robot Arm Based on Error Compensation Using BP Neural Network." International Journal of Humanoid Robotics 16, no. 05 (2019): 1950024. http://dx.doi.org/10.1142/s0219843619500245.

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This paper proposes an external force detection method for humanoid robot arm without using joint torque sensors, which can detect the external force of the joint space in real time during the operation of the robot. We first analyzed the structure of the humanoid robot arm we designed, and then established the external force detection model of the robot arm based on robot dynamics and motor dynamics. Subsequently, analyses were conducted on the error of the detection model and the dynamic model error of the robot arm is compensated by using the artificial neural network method to obtain more

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40

Huang, Lu, Honglian Guo, Kunlong Li, Yuhui Chen, Baohua Feng, and Zhi-Yuan Li. "Three dimensional force detection of gold nanoparticles using backscattered light detection." Journal of Applied Physics 113, no. 11 (2013): 113103. http://dx.doi.org/10.1063/1.4795272.

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41

Pang, Caoyuan, Jianting Zhou, Ruiqiang Zhao, Hu Ma, and Yi Zhou. "Research on Internal Force Detection Method of Steel Bar in Elastic and Yielding Stage Based on Metal Magnetic Memory." Materials 12, no. 7 (2019): 1167. http://dx.doi.org/10.3390/ma12071167.

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Based on the metal magnetic memory effect, this paper proposed a new non-destructive testing method for the internal tensile force detection of steel bars by analyzing the self-magnetic flux leakage (SMFL) signals. The variation of the SMFL signal of the steel bar with the tensile force indicates that the curve of the SMFL signal has a significant extreme point when the tensile force reaches about 65% of the yield tension, of which the first derivative curve has extreme points in the elastic and yielding stages, respectively. To study the variation of SMFL signal with the axial position of the

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42

Bergin, Michael, Michael Sheedy, Peter Ross, Grant Wylie, and Philip Bird. "Measuring the Forces of Middle Ear Surgery; Evaluating a Novel Force-Detection Instrument." Otology & Neurotology 35, no. 2 (2014): e77-e83. http://dx.doi.org/10.1097/mao.0000000000000173.

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43

Watanabe, S., K. Hane, M. Ito, and T. Goto. "Dynamic mode force microscopy for the detection of lateral and vertical electrostatic forces." Applied Physics Letters 63, no. 18 (1993): 2573–75. http://dx.doi.org/10.1063/1.110437.

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44

Feng, Lihang, Wei Chen, Ti Wu, et al. "An improved sensor system for wheel force detection with motion-force decoupling technique." Measurement 119 (April 2018): 205–17. http://dx.doi.org/10.1016/j.measurement.2018.01.066.

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45

Yamamoto, Shin-ichi, Hirofumi Yamada, and Hiroshi Tokumoto. "Precise force curve detection system with a cantilever controlled by magnetic force feedback." Review of Scientific Instruments 68, no. 11 (1997): 4132–36. http://dx.doi.org/10.1063/1.1148357.

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46

Pile, J., G. B. Wanna, and N. Simaan. "Robot-assisted perception augmentation for online detection of insertion failure during cochlear implant surgery." Robotica 35, no. 7 (2016): 1598–615. http://dx.doi.org/10.1017/s0263574716000333.

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SUMMARYDuring the past decade, robotics for cochlear implant electrode array insertion has been limited to manipulation assistance. Going beyond manipulation assistance, this paper presents the new concept of perception augmentation to detect and warn against the onset of intracochlear electrode array tip folding. This online failure detection method uses a combination of intraoperative electrode insertion force data and a predictive model of insertion force profile progression as a function of insertion depth. The predictive model uses statistical characterization of insertion force profiles

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47

Altintas, Y., I. Yellowley, and J. Tlusty. "The Detection of Tool Breakage in Milling Operations." Journal of Engineering for Industry 110, no. 3 (1988): 271–77. http://dx.doi.org/10.1115/1.3187881.

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The paper describes a rather simple and efficient algorithm for processing the milling force signal to detect cutter breakage. Using sampling synchronized with cutter teeth the basic variation per tooth is removed by calculating average forces per tooth. The first difference of these forces detects both breakage and some sudden changes in cutting conditions (cornering, milling over a slot). The second difference distinguishes between the two. The algorithm is illustrated by computational simulations as well as by measurements in milling tests.

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48

Smith, Allan M. "Some shear facts and pure friction related to roughness discrimination and the cutaneous control of grasping." Canadian Journal of Physiology and Pharmacology 72, no. 5 (1994): 583–90. http://dx.doi.org/10.1139/y94-083.

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The question of whether friction contributes to the perception of roughness has been overdebated and underinvestigated. A review of the psychophysical literature suggests that roughness and friction can be subjectively distinguished very effectively, although the same rapidly adapting Meissner corpuscles (RA1s) and slowly adapting Merkel receptors (SA1s) are stimulated by both stimuli. It appears that to achieve the subjective appreciation of roughness, the brain must learn to ignore variations in the speed of movement over the skin, the perpendicular force applied to the receptor surface, and

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49

Bell, Audrey K., and Caroline G. L. Cao. "How Does Artificial Force Feedback Affect Laparoscopic Surgery Performance?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 11 (2007): 646–50. http://dx.doi.org/10.1177/154193120705101109.

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The use of haptic devices to provide force feedback in teleoperation has been shown to enhance performance. An experiment was conducted to examine whether artificial force feedback is utilized in the same manner as real force feedback in a simulated laparoscopic tissue-probing task. Forces in probing a double-layer silicon gel mass were replicated and exaggerated in a virtual environment using a haptic device. Ten subjects performed the probing task in four different conditions: 1) realistic force feedback, 2) exaggerated feedback, 3) disproportionately exaggerated forces, and 4) reversed and

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50

Carey, Ciaran H., Eugene J. O'Brien, and Jennifer Keenahan. "Investigating the Use of Moving Force Identification Theory in Bridge Damage Detection." Key Engineering Materials 569-570 (July 2013): 215–22. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.215.

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This paper investigates the use of Moving Force Identification as a method of bridge damage detection. It identifies changes in the predicted axle force histories that occur as a result of loss in bridge element stiffness, i.e. as a result of bridge damage. A 2-dimensional Vehicle-Bridge Interaction model is used in numerical simulations to assess the effectiveness of the method in detecting changes in stiffness. Fleets of similar vehicles are simulated and the mean force pattern is used as the damage indicator. Results show that the method is more sensitive to damage than direct measurements

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