Littérature scientifique sur le sujet « MACHINING VARIABLES »
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Articles de revues sur le sujet "MACHINING VARIABLES"
Neto, João Cirilo da Silva, Evaldo Malaquias da Silva et Marcio Bacci da Silva. « Intervening variables in electrochemical machining ». Journal of Materials Processing Technology 179, no 1-3 (octobre 2006) : 92–96. http://dx.doi.org/10.1016/j.jmatprotec.2006.03.105.
Texte intégralChuri, N. J., Z. J. Pei et C. Treadwell. « ROTARY ULTRASONIC MACHINING OF TITANIUM ALLOY : EFFECTS OF MACHINING VARIABLES ». Machining Science and Technology 10, no 3 (septembre 2006) : 301–21. http://dx.doi.org/10.1080/10910340600902124.
Texte intégralCong, W. L., Z. J. Pei, Timothy Deines, Q. G. Wang et Clyde Treadwell. « Rotary Ultrasonic Machining of stainless steels : empirical study of machining variables ». International Journal of Manufacturing Research 5, no 3 (2010) : 370. http://dx.doi.org/10.1504/ijmr.2010.033472.
Texte intégralNandhakumar, S., S. Sathish Kumar et K. Sakthivelu. « Optimization of Machining Variables in Electric Discharge Machining Using Stainless Steel 317 in Full Factorial Method ». Mechanics and Mechanical Engineering 22, no 1 (12 août 2020) : 105–18. http://dx.doi.org/10.2478/mme-2018-0010.
Texte intégralFrumuşanu, Gabriel, et Alexandru Epureanu. « HOLISTIC MONITORING OF MACHINING SYSTEM ». International Journal of Modern Manufacturing Technologies 13, no 3 (25 décembre 2021) : 45–53. http://dx.doi.org/10.54684/ijmmt.2021.13.3.45.
Texte intégralMehrvar, Ali, Ali Basti et Ali Jamali. « Inverse modelling of electrochemical machining process using a novel combination of soft computing methods ». Proceedings of the Institution of Mechanical Engineers, Part C : Journal of Mechanical Engineering Science 234, no 17 (2 avril 2020) : 3436–46. http://dx.doi.org/10.1177/0954406220916495.
Texte intégralChainawakul, Adirake, Koji Teramoto et Hiroki Matsumoto. « Statistical Modelling of Machining Error for Model-Based Elastomer End-Milling ». International Journal of Automation Technology 15, no 6 (5 novembre 2021) : 852–59. http://dx.doi.org/10.20965/ijat.2021.p0852.
Texte intégralPaul, Lijo, et S. Hiremath Somashekhar. « Effect of Process Parameters on Heat Affected Zone in Micro Machining of Borosilicate Glass Using μ-ECDM Process ». Applied Mechanics and Materials 592-594 (juillet 2014) : 224–28. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.224.
Texte intégralPradhan, Mohan Kumar, et Chandan Kumar Biswas. « Response Surface Analysis of EDMED Surfaces of AISI D2 Steel ». Advanced Materials Research 264-265 (juin 2011) : 1960–65. http://dx.doi.org/10.4028/www.scientific.net/amr.264-265.1960.
Texte intégralRehman, Gulfam Ul, Syed Husain Imran Jaffery, Mushtaq Khan, Liaqat Ali, Ashfaq Khan et Shahid Ikramullah Butt. « Analysis of Burr Formation in Low Speed Micro-milling of Titanium Alloy (Ti-6Al-4V) ». Mechanical Sciences 9, no 2 (20 juillet 2018) : 231–43. http://dx.doi.org/10.5194/ms-9-231-2018.
Texte intégralThèses sur le sujet "MACHINING VARIABLES"
Duong, Tan Quang. « Commande à gains variables de l’erreur de contour pour l’usinage multiaxes ». Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC009/document.
Texte intégralThe advanced machining techniques are always the backbone of the manufacturing industries. Among such techniques, high speed machining is the main subject of this PhD thesis. Indeed, the main objective of this work is to improve the contouring accuracy in multi-axis high speed machining of free-form surfaces, directly acting inside the axis control loops. To do that, a first step aims at elaborating a strategy to estimate as accurately as possible the contour error for different tool configurations. This contour error is then minimized by means of an off-line adaptation for a given profile of the proportional and feedforward gains of the axis position loop controllers. This gain adaptation is performed via an optimization algorithm that considers a nonlinear model of the machine behaviour, in particular including friction related to each axis. This optimization leading to the controllers gains takes into account several constraints, including the axis kinematic (velocity, acceleration and jerk) limitations, the stability of the controlled loops and the motor current limits. Finally, to help their integration within an industrial framework, the developed strategies can be directly implemented in commercial CNC, by exploiting all possibilities of the classical control structure of axis drive
Hussein, Wessam Mahmoud Elbestawi Mohamed A. A. Veldhuis Stephen C. « Machining process monitoring using multivariate latent variable methods ». *McMaster only, 2007.
Trouver le texte intégralNagaraj, Arjun. « ANALYSIS OF SURFACE INTEGRITY IN MACHINING OF CFRP UNDER DIFFERENT COOLING CONDITIONS ». UKnowledge, 2019. https://uknowledge.uky.edu/me_etds/142.
Texte intégralLaforce, Francois. « Zlepšení výroby aeronautické součásti ». Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229843.
Texte intégralYADAV, MANISH KUMAR. « EFFECTS OF MACHINING VARIABLES ON SURFACE ROUGHNESS IN WIRE-EDM ». Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14366.
Texte intégralYADAV, MANEESH KUMAR. « EFFECTS OF MACHINING VARIABLES ON SURFACE ROUGHNESS IN WIRE-EDM OF AISI D3 ». Thesis, 2011. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19702.
Texte intégralPraharaj, Ami t. Kumar. « Simulation of Electrolyte Flow Pattern and Variation of MRR With Machining Variables in ECM for L-shaped Tool using CFD ». Thesis, 2018. http://ethesis.nitrkl.ac.in/9702/1/2018_MT_216ME2374_AKPraharaj_Simulation.pdf.
Texte intégralChen, Hsin-Hung, et 陳信宏. « On the design of tool profiles for machining line generated variable pitch helicoids ». Thesis, 1993. http://ndltd.ncl.edu.tw/handle/22367187145909021800.
Texte intégralSheng, Yu-Chun, et 盛玉君. « Studies on the Numerical Control Programming for Machining Variable Pitch Lead Screw on Four-AXIS MACHINE ». Thesis, 1993. http://ndltd.ncl.edu.tw/handle/38829237727428045433.
Texte intégral國立成功大學
機械工程研究所
81
In this thesis , two kinds of tool path generation methods are developed for machining Variable Pitch Lead Screw with four cylindrical meshing elements (V.P.L.S.) on four-axis numerical control machine . The first method employs end-mill cutter , According to the geometry of V.P.L.S. mechanism , the machine tool settings is calculated and the tool path is generated , A 4-axis machine center with angle head attachment is used for the machining of the screw The second method employs ball-end mill cutter, Through the coordinate transformation , the surface normal vector operation and the tool offset calculation , this new method combined surface generating with surface sculpturing for the V.P.L.S. surface . Finally , an example for N C milling of Variable Pitch Lead Screw with four cylindrical meshing elements is presented . The results show that the mathematical errors of V.P.L.S. surface can be controlled within tolerance by the proposed methods .
HONG, CHING-HUA, et 洪清華. « Application of Deep Learning to the Intelligent Control of Wire Electrical Discharge Machining of Variable Thickness Workpiece ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/72npak.
Texte intégral國立臺灣大學
機械工程學研究所
107
The purpose of this study is to establish a deep learning intelligent control system that can combine processing efficiency, stability and kerf consistency in variable thickness processing. In the past, the wire electrical discharge machining Control for variable thickness workpiece, and focus on improving processing efficiency and stability. The research on the problem that the kerf difference affects the accuracy of the variable thickness workpiece is relatively lacking. The true straightness is not good after processing, and it may lead to error fitting of future workpieces. Deep Learning is used to automatically extract features that are representative of data characteristics by transforming data through multiple nonlinear hidden layers, that is, from a variety of discharge signals that are highly correlated with the thickness of the workpiece. The recessive feature function has strong robustness and is not sensitive to noise or some of extreme values, so the thickness of the workpiece can be accurately known. The second stage is the setting of processing parameters. In the past, it relied on experienced operators. Therefore, the deep learning technique was used again to normalize the hidden features of the input data, so that the model output is like a experience operator to determination what the parameters should be setting. Intelligent control strategy not only reduce the operator''s technical threshold, but also achieve a goal of uniform material removal rate during the trim cutting and reduce the occurrence of line marks. The experimental results show that the deep learning workpiece thickness online estimation system established in this study which can accurately estimate the thickness of the workpiece, and the processing parameter intelligent control system can adjust the discharge TOFF and TAOFF according to different workpiece thicknesses. The discharge frequency is controlled within the desired value, and the processing efficiency can be effectively improved when the thickness of the workpiece is thin to thick; when the thickness of the workpiece is thick to thin, the feed rate will be accelerated and the wire is broken by discharge concentration phenomenon. On the other hand, the strategy of this study effectively balances the kerf uniformity, that is, the average value and variation of the kerf of each class thickness can be effectively maintained within a stable value when processing different workpiece thicknesses.
Chapitres de livres sur le sujet "MACHINING VARIABLES"
Davis, Rahul, Abhishek Singh, Tanya Singh, Subham Chhetri, V. Vikali Sumi, Alomi P. Zhimomi et Stephen Dilip Mohapatra. « Optimization of Input Control Variables in Electric Discharge Machining of Inconel-718 ». Dans Lecture Notes in Mechanical Engineering, 541–49. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2696-1_53.
Texte intégralThellaputta, Gopala Rao, P. S. C. Bose, C. S. P. Rao et C. S. Raju. « Effect of Machining Variables on Cutting Temperature While Rotary Milling of Inconel 625 ». Dans Lecture Notes on Multidisciplinary Industrial Engineering, 27–36. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7643-6_3.
Texte intégralSonawane, Sachin Ashok, et Akshay P. Pawar. « Effect of Cutting Variables of End Milling Process on Surface Roughness and Machining Vibrations ». Dans Techno-Societal 2018, 661–70. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16962-6_67.
Texte intégralSanthi Priya, P., Subramanyam Pavuluri et Yogesh Madaria. « Experimental Investigations of Process Variables on Wire Electrical Discharge Machining (WEDM) of AISI 52100 Steel ». Dans Lecture Notes in Mechanical Engineering, 571–80. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7909-4_53.
Texte intégralPramanik, D., A. S. Kuar et D. Bose. « Effects of Wire EDM Machining Variables on Material Removal Rate and Surface Roughness of Al 6061 Alloy ». Dans Renewable Energy and its Innovative Technologies, 231–41. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2116-0_19.
Texte intégralSharma, Khushboo, Jai Kishan Sambharia et Alok Khatri. « Optimization of Process Variables in Plasma Arc Machining of Inconel-718 Alloy Using Taguchi with Grey Relational Analysis ». Dans Lecture Notes in Mechanical Engineering, 37–58. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9117-4_4.
Texte intégralDas, Sudhansu Ranjan, et Anshuman Das. « Study the Influences of Various Input Variables on Material Removal Rate During μEDM Machining of Super Alloy Material ». Dans Lecture Notes in Mechanical Engineering, 681–92. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4606-6_62.
Texte intégralMadhavarao, S., Ravi Varma Penmetsa, Ch Rama Bhadri Raju et Hema T. Raju Gottumukkala. « Optimization of Process Variables in Abrasive Water Jet Machining of Nimonic C-263 Super Alloy Using Taguchi Method ». Dans Advances in Sustainability Science and Technology, 167–77. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4321-7_15.
Texte intégralYadav, Rajat, et Pradeep Kumar Singh. « Variation in Unevenness of Surface in Machining Ti–6Al–4 V Due to Change in Key Variables of Micro-Electric Discharge Milling Process ». Dans Lecture Notes in Mechanical Engineering, 1083–91. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2188-9_99.
Texte intégralNandurkar, Santosh, Sachin Kulkarni, Tushar Hawal, Niranjan Pattar et Nagaraj Kelageri. « Investigation of Effect of EDM Process Variables on Material Removal Rate and Tool Wear Rate in Machining of EN19 Steel Using Response Surface Methodology ». Dans Lecture Notes in Mechanical Engineering, 71–82. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2278-6_7.
Texte intégralActes de conférences sur le sujet "MACHINING VARIABLES"
Cong, W. L., Q. Feng, Z. J. Pei, T. W. Deines et C. Treadwell. « Dry Machining of Carbon Fiber Reinforced Plastic Composite by Rotary Ultrasonic Machining : Effects of Machining Variables ». Dans ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50116.
Texte intégralChen, Xiaoxu, Hui Wang, Yingbin Hu, Dongzhe Zhang, Weilong Cong et Anthony R. Burks. « Rotary Ultrasonic Machining of CFRP Composites : Effects of Machining Variables on Workpiece Delamination ». Dans ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-3019.
Texte intégralQin, Na, Z. J. Pei et D. M. Guo. « Ultrasonic-Vibration-Assisted Grinding of Titanium : Cutting Force Modeling With Design of Experiments ». Dans ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84325.
Texte intégralNing, Fuda, et Weilong Cong. « Rotary Ultrasonic Machining of CFRP : Design of Experiment With a Cutting Force Model ». Dans ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9227.
Texte intégralZeng, W. M., Z. C. Li, N. J. Churi, Z. J. Pei et C. Treadwell. « Experimental Investigation Into Rotary Ultrasonic Machining of Alumina ». Dans ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61700.
Texte intégralLo, Chih-Hao, Jun Ni et Jingxia Yuan. « Thermal Sensor Placement Strategy for Machine Error Compensation ». Dans ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0355.
Texte intégralKumanotani, Maho, Hitoshi Kusino et Keiichi Nakamoto. « Proposal of a Tool Path Generation Method to Ensure Workpiece Stiffness for Efficient Rough Machining ». Dans JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/lemp2020-8543.
Texte intégralLee, Cheol W. « Multirate Estimation for the Machining Process Under Multirate Noise ». Dans ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42187.
Texte intégralZhang, Dongzhe, Hui Wang, Yingbin Hu, Xiaoxu Chen, Weilong Cong et Anthony R. Burks. « Rotary Ultrasonic Machining of CFRP Composites : Effects of Carbon Fiber Reinforcement Structure ». Dans ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-3014.
Texte intégralWentz, John E., et Andrew J. Coleman. « Energy Analysis of Machining and Machining Facilities Based on the Micro-Factory Concept ». Dans ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7222.
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