Academic literature on the topic 'Machine FZG'
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Journal articles on the topic "Machine FZG"
Höhn, B. R., and H. Winter. "Laboratories at work: Institute for machine elements, Gear Research Centre (FZG)." Tribotest 3, no. 3 (March 1997): 325–40. http://dx.doi.org/10.1002/tt.3020030306.
Full textHargreaves, D. J., and Anton Planitz. "Assessing the energy efficiency of gear oils via the FZG test machine." Tribology International 42, no. 6 (June 2009): 918–25. http://dx.doi.org/10.1016/j.triboint.2008.12.016.
Full textWinter, H. "Integrating Universities and Industry—A German Approach." Proceedings of the Institution of Mechanical Engineers, Part B: Management and engineering manufacture 202, no. 1 (February 1988): 9–17. http://dx.doi.org/10.1243/pime_proc_1988_202_041_02.
Full textMassocchi, Davide, Marco Lattuada, Steven Chatterton, and Paolo Pennacchi. "SRV Method: Lubricating Oil Screening Test for FZG." Machines 10, no. 8 (July 28, 2022): 621. http://dx.doi.org/10.3390/machines10080621.
Full textAyel, J., Y. Kraus, and J. P. Michel. "Séverisation de l'essai de capacité de charge des lubrifiants sur machine a engrenages FZG." Revue de l'Institut Français du Pétrole 40, no. 6 (November 1985): 831–42. http://dx.doi.org/10.2516/ogst:1985049.
Full textDurand de Gevigney, J., C. Changenet, F. Ville, and P. Velex. "Thermal modelling of a back-to-back gearbox test machine: Application to the FZG test rig." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 226, no. 6 (January 16, 2012): 501–15. http://dx.doi.org/10.1177/1350650111433243.
Full textTao, J., T. G. Hughes, H. P. Evans, R. W. Snidle, N. A. Hopkinson, M. Talks, and J. M. Starbuck. "Elastohydrodynamic Lubrication Analysis of Gear Tooth Surfaces From Micropitting Tests." Journal of Tribology 125, no. 2 (March 19, 2003): 267–74. http://dx.doi.org/10.1115/1.1510881.
Full textHlebanja, Gorazd. "Gradual development of S-shaped gears." MATEC Web of Conferences 366 (2022): 01001. http://dx.doi.org/10.1051/matecconf/202236601001.
Full textArri, Harwant Singh, Ramandeep Singh, Sudan Jha, Deepak Prashar, Gyanendra Prasad Joshi, and Ill Chul Doo. "Optimized Task Group Aggregation-Based Overflow Handling on Fog Computing Environment Using Neural Computing." Mathematics 9, no. 19 (October 7, 2021): 2522. http://dx.doi.org/10.3390/math9192522.
Full textAlalibo, Belema P., Bing Ji, and Wenping Cao. "Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology." Energies 15, no. 2 (January 14, 2022): 577. http://dx.doi.org/10.3390/en15020577.
Full textDissertations / Theses on the topic "Machine FZG"
Grenet, de Bechillon Nicolas. "Approche multi-échelles pour l'étude du grippage des dentures d'engrenages." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0024.
Full textEnvironmental concerns are driving the aerospace industry to innovate and develop new technologies to achieve sustainable aviation. Among these innovations, the next generation of civil engines requires the integration of gearboxes within them. In order to design a reliable product, different failure modes, such as gear scuffing, must be taken into account. Scuffing is a sudden gear failure where material is transferred from one surface to another. This transfer is caused by local surface welding during meshing. Scuffing leads to degradation of the tooth surface, which reduces gear efficiency. Although this mode of gear failure has been extensively studied, there are no commonly accepted initiation criteria. Therefore, physical understanding of scuffing initiation is needed. The first part of this study focused on the role of roughness. A numerical model was set up to evaluate the temperatures reached locally in the contact zone. The calculations carried out show that these last ones at the roughness scale do not seem able to explain the formation of micro-welds by fusion of the surface asperities in a lubricated contact. Scuffing therefore appear to be the consequence of a potential break in the lubricant film. In a second part, this film breakage was studied experimentally on a twin-disk machine. A procedure was developed to study the phenomenon by acting on the lubricant film thickness. The performed tests seem to show that the breakdown of the lubricating film is governed by its temperature, which depends directly on the operating conditions. Thus, a scuffing criterion was established on discs.In the last part, gear tests were carried out. It was shown, as for disc tests, that total temperature alone does not predict scuffing. However, the criterion developed on discs does not seem to be able to explain tooth scuffing. Since no criteria seem to be able to explain the scuffing, a new approach is proposed. Finally, conclusions and prospects are proposed. The chronology of the scuffing initiation mechanism are recalled. The prospects aim, on the one hand, to improve the representativeness of the tests on discs compared to gears, in particular with regard to the geometry of the surface roughness; and, on the other hand, to analyse in detail and experimentally the hypothesis of the lubricating film breakage as a mechanism of scuffing initiation
Badokhon, Alaa. "An Adaptable, Fog-Computing Machine-to-Machine Internet of Things Communication Framework." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1492450137643915.
Full textHolas, Jiří. "Modernizace řízení frézky FNG." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442843.
Full textGullo, Thomas W. "A Methodology to Evaluate the Dynamic Behavior of Back-to-back Test Machines." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555588592218025.
Full textLu, Shen. "Early identification of Alzheimer's disease using positron emission tomography imaging and machine learning." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23735.
Full textEgli, Sebastian [Verfasser], and Jörg [Akademischer Betreuer] Bendix. "Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning / Sebastian Egli ; Betreuer: Jörg Bendix." Marburg : Philipps-Universität Marburg, 2019. http://d-nb.info/1187443476/34.
Full textDi, Donato Davide. "Sviluppo, Deployment e Validazione Sperimentale di Architetture Distribuite di Machine Learning su Piattaforma fog05." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19021/.
Full textAnjum, Ayesha. "Differentiation of alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach." Toulouse 3, 2013. http://thesesups.ups-tlse.fr/2238/.
Full textWe used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes. The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set. We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease
Dukart, Jürgen. "Contribution of FDG-PET and MRI to improve Understanding, Detection and Differentiation of Dementia." Doctoral thesis, Universitätsbibliothek Leipzig, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-66495.
Full textCastellanos, Carlos. "Development of a validation shape sensing algorithm in Python with predictive and automatedanalysis." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454942.
Full textBooks on the topic "Machine FZG"
Free cash flow: Seeing through the accounting fog machine to find great stocks. Hoboken, N.J: Wiley, 2009.
Find full textDie Entwicklung der Firma Kugelfischer, Georg Schäfer & Co.: Unter besonderer Berücksichtigung der Kontinuität als Familienunternehmen und die regionalen Auswirkungen ihrer Entwicklung aus betriebs- und industriebezogener Sicht. Würzburg: Creator, 1988.
Find full textFandel, G. Modern Production Concepts: Theory and Applications Proceedings of an International Conference, Fernuniversität, Hagen, FRG, August 20-24, 1990. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.
Find full textFog Machine. Lucky Sky Press, 2014.
Find full textFog Machine. Lucky Sky Press, 2014.
Find full textThe fog machine: A novel. 2014.
Find full textMisra, Sudip, Subhadeep Sarkar, and Subarna Chatterjee. Sensors Cloud and Fog. Taylor & Francis Group, 2019.
Find full textChristy, George C. Free Cash Flow: Seeing Through the Accounting Fog Machine to Find Great Stocks. Wiley & Sons, Limited, John, 2011.
Find full textErgonomic Data for Equipment Design: Proceedings of the NATO ARI held in Munich, FRG, March 22-26, 1982 (Nato Conference Series III, Vol 25: Human Factors). Springer, 1985.
Find full textMisra, Sudip, Subhadeep Sarkar, and Subarna Chatterjee. Sensors, Cloud, and Fog: The Enabling Technologies for the Internet of Things. Taylor & Francis Group, 2019.
Find full textBook chapters on the topic "Machine FZG"
Thomas, Priya, and Deepa V. Jose. "Edge/Fog Computing." In Machine Intelligence, 47–64. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003424550-3.
Full textLohani, Kaustubh, Prajwal Bhardwaj, and Ravi Tomar. "Fog Computing and Machine Learning." In Fog Computing, 133–51. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-10.
Full textJaiswal, Kabir, and Niharika Singh. "Application of Machine Learning in Fog Computing." In Fog Computing, 41–50. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-4.
Full textGaba, Smriti, Susheela Dahiya, Samarth Vashisht, and Avita Katal. "The Use of Machine Learning in Fog Computing." In Fog Computing, 27–39. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-3.
Full textMoh, Melody, and Robinson Raju. "Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems." In Fog and Edge Computing, 223–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119525080.ch10.
Full textYan, Xuan, Xiaolong Xu, Yu Zheng, and Fei Dai. "Fog Server Placement for Multimodality Data Fusion in Neuroimaging." In Machine Learning for Cyber Security, 234–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_20.
Full textGutiérrez, Norma, Eva Rodríguez, Sergi Mus, Beatriz Otero, and Ramón Canal. "Privacy Preserving Deep Learning Framework in Fog Computing." In Machine Learning, Optimization, and Data Science, 504–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_45.
Full textPinto, Manuel, Nicola Roveri, Gianluca Pepe, Andrea Nicoletti, Gabriele Balconi, and Antonio Carcaterra. "Extraction of the Beam Elastic Shape from Uncertain FBG Strain Measurement Points." In Mechanisms and Machine Science, 362–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03320-0_39.
Full textMartin, John Paul, Christina Terese Joseph, K. Chandrasekaran, and A. Kandasamy. "Machine Learning Powered Autoscaling for Blockchain-Based Fog Environments." In Blockchain and Applications, 281–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86162-9_28.
Full textPrasad, Devendra, Pradeep Singh Rawat, and Neeraj Rathore. "Optimized Cloud Storage Data Analysis Using the Machine Learning Model." In Bio-Inspired Optimization in Fog and Edge Computing Environments, 165–84. New York: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003322931-10.
Full textConference papers on the topic "Machine FZG"
Michalczewski, Remigiusz, Marian Szczerek, Waldemar Tuszynski, and Jan Wulczynski. "The Scuffing Resistance of the Coated Tribosystems Lubricated With Ecological Oils." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-63432.
Full textGai, Yuxian, Huiying Liu, and Shen Dong. "Vibration Control System for a Sub-Micro Ultra-Precision Turning Machine." In 2007 First International Conference on Integration and Commercialization of Micro and Nanosystems. ASMEDC, 2007. http://dx.doi.org/10.1115/mnc2007-21040.
Full textWu, Dazhong, Janis Terpenny, Li Zhang, Robert Gao, and Thomas Kurfess. "Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8559.
Full textArandjelovic, Ognjen, and Roberto Cipolla. "Colour invariants for machine face recognition." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813306.
Full textGoncalves, Diogo, Karima Velasquez, Marilia Curado, Luiz Bittencourt, and Edmundo Madeira. "Proactive Virtual Machine Migration in Fog Environments." In 2018 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2018. http://dx.doi.org/10.1109/iscc.2018.8538655.
Full textBittencourt, Luiz Fernando, Marcio Moraes Lopes, Ioan Petri, and Omer F. Rana. "Towards Virtual Machine Migration in Fog Computing." In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2015. http://dx.doi.org/10.1109/3pgcic.2015.85.
Full textLi-Xia Xie, Hong-Yu Yang, and Yi Lu. "FUG based intelligent query for audit database." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620837.
Full textHu, Ching-Piao, and C. F. Liao. "Optical holographic Hough processor for machine vision." In 15th Int'l Optics in Complex Sys. Garmisch, FRG, edited by F. Lanzl, H. J. Preuss, and G. Weigelt. SPIE, 1990. http://dx.doi.org/10.1117/12.34930.
Full textFoukalas, Fotis, and Athanasios Tziouvaras. "A Federated Machine Learning Protocol for Fog Networks." In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2021. http://dx.doi.org/10.1109/infocomwkshps51825.2021.9484485.
Full textIngistov, Steve. "Fog System Performance in Power Augmentation of Heavy Duty Power Generating Gas Turbines Model 7EA." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0305.
Full textReports on the topic "Machine FZG"
Michaelis, K., and H. Winter. Development of a High Temperature FZG-Ryder Gear Lubricant Load Capacity Machine. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada210799.
Full textAlber, Charlotte, Laura Dusl, Brigitte Ecker, and Sabine Pohoryles-Drexel. Erfahrungen und Ergebnisse aus der begleitenden Erhebung zum Pilot w-fFORTE Innovatorinnen. BMDW, July 2021. http://dx.doi.org/10.22163/fteval.2021.523.
Full textWarta, Katharina, Tobias Dudenbostel, María del Carmen Calatrava Moreno, Francesca Guadagno, Simon Zingerle, Sandra Skok, and Harald Grill. Evaluierung des COMET-Programms. Technopolis Group - Austria, June 2021. http://dx.doi.org/10.22163/fteval.2022.524.
Full textKirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.
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