Academic literature on the topic 'Machining sound'
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Journal articles on the topic "Machining sound"
Rafighi, Mohammad. "The cutting sound effect on the power consumption, surface roughness, and machining force in dry turning of Ti-6Al-4V titanium alloy." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 6 (February 23, 2022): 3041–57. http://dx.doi.org/10.1177/09544062211072411.
Full textAKKUŞ, HARUN. "MULTIPLE OPTIMIZATION ANALYSIS OF MRR, SURFACE ROUGHNESS, SOUND İNTENSITY, ENERGY CONSUMPTION, AND VIBRATION VALUES IN MACHINABILITY OF TC4 TITANIUM ALLOY." Surface Review and Letters 28, no. 09 (April 30, 2021): 2150072. http://dx.doi.org/10.1142/s0218625x21500724.
Full textDong, Quan Cheng, Chang Sheng Ai, and Na Wang. "The Study of Tool Wear and Breakage Based on the Characteristic Analysis of Acoustic Spectrum." Materials Science Forum 532-533 (December 2006): 197–200. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.197.
Full textYang, Xuefeng, Xulin Cai, Wenan Yang, and Youpeng You. "Novel Tool Path Generation Method for Pocket Machining Using Sound Field Synthesis Theory." Machines 11, no. 2 (January 18, 2023): 131. http://dx.doi.org/10.3390/machines11020131.
Full textSun, Meng Zi, Fei Huang, and Hua Jing Li. "The Influence of Structural Parameters on the Sound Absorption Performance of Micro Perforated Absorber." Applied Mechanics and Materials 701-702 (December 2014): 424–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.424.
Full textZajac, Jozef, Zuzana Hutyrová, and Imrich Orlovský. "Investigation of Surface Roughness after Turning of One Kind of the Bio-Material with Thermoplastic Matrix and Natural Fibers." Advanced Materials Research 941-944 (June 2014): 275–79. http://dx.doi.org/10.4028/www.scientific.net/amr.941-944.275.
Full textBhandari, Binayak. "Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals." Micromachines 12, no. 12 (November 29, 2021): 1484. http://dx.doi.org/10.3390/mi12121484.
Full textHao, Wangs Shen, Xun Sheng Zhu, Biao Jun Tian, and M. R. Chi. "Autoregressive Spectrum Analysis of Vibration and Condition Monitoring of Self-Propelled Rotary Tool." Key Engineering Materials 329 (January 2007): 743–48. http://dx.doi.org/10.4028/www.scientific.net/kem.329.743.
Full textMiyazaki, T., Y. Tanaka, T. Tokunaga, and N. Taniguchi. "Controlling of Q-Switched YAG Laser Beam Machining by Generated Sound." CIRP Annals 42, no. 1 (1993): 243–46. http://dx.doi.org/10.1016/s0007-8506(07)62435-4.
Full textCasal-Guisande, Manuel, Alberto Comesaña-Campos, Alejandro Pereira, José-Benito Bouza-Rodríguez, and Jorge Cerqueiro-Pequeño. "A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring." Mathematics 10, no. 3 (February 6, 2022): 520. http://dx.doi.org/10.3390/math10030520.
Full textDissertations / Theses on the topic "Machining sound"
Kulhánek, Jaroslav. "Aplikace měřicích sond v procesu obrábění." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228889.
Full textНагорний, Володимир В`ячеславович, Владимир Вячеславович Нагорный, and Volodymyr Viacheslavovych Nahornyi. "Контроль динамического поведения металлообрабатывающих технологических систем и метод определения их ресурса." Thesis, Изд-во СумГУ, 2015. http://essuir.sumdu.edu.ua/handle/123456789/42598.
Full textРабота посвящена актуальной проблеме контроля в режиме реального времени динамического поведения обрабатывающих систем и развитию методов определения их ресурса. В работе впервые поставлена и решена научно-техническая задача расчётно-экспериментального исследования динамического поведения обрабатывающих систем в зависимости от изменения технического состояния их элементов(станка, режущего инструмента и детали). Это позволило получить новое решение научно-технической задачи контроля динамического поведения обрабатывающих систем и определения их ресурса в условиях, когда «Нормы…», регламентирующие степень критичности их динамического поведения отсутствуют. В качестве информационного сигнала, косвенно характеризующего качество функционирования обрабатывающих систем, выбран звук, сопровождающий процесс их работы. Как показали исследования, тренд звукового сигнала подобен кривой износа режущего инструмента, а его временная реализация и частотный спект, совпадают с профилем шероховатости и ее частотным спектром, что позволяет звуку наиболее полно отражать динамическое поведение обрабатывающих систем. Разработана прогнозная модель, особенность которой заключается в том, что искомый ресурс обрабатывающей системы включен в ее математическую структуру и определяется при идентификации модели по результатам мониторинга тренда звука, сопровождающего процесс работы обрабатывающей системы. Получены расчетные зависимости для показателей состояния, позволяющих в понятиях теории нечетких множеств отнести с точки зрения динамики разнообразные технологические условия работы обрабатывающих систем к ряду стандартних, что послужило основой для разработки алгоритма контроля динамического состояния обрабатывающей системы. Процесс контроля был автоматизирован за счет использования микропроцессорного устройства, содержащего программный модуль, формализующий указанный алгоритм контроля. Автоматизация процесса контроля позволила реализовать в практике реального производства результаты диссертационной работы, что обеспечило в режиме реального времени контроль качества обработки детали и технического состояния режущего инструмента и станочного оборудования. Методы исследований: методы теории колебаний, идентификации и математической статистики. Разработка алгоритма контроля динамического поведения обрабатывающих систем и определения на этой основе их ресурса осуществлялось на основе методов информационных технологий. Анализ экспериментальных данных проводился на основе теории цифровой обработки сигналов. Для решения задач определения ресурса использованы методы случайного поиска.
The work is devoted to the actual problem of control dynamic behavior of metalprocessing systems and method of determining their life. For the first time solved the scientific and technical problem of forecasting resource of the metal-processing systems in the course of their work turning without the use of statistical data on the working time of the metal-processing systems to replace it. This problem is solved with the help of a predictive model, in the mathematical structure, which enabled the desired metalprocessing systems life. A resource is defined in the identification process model, based on the results of monitoring the trend of information parameter, accompanying the working process. In order to implement operational control of the mashinig developed forecasting system was automated with the help of specially developed for this purpose microprocessor-based controlo - prognostic system, that allows simultaneous with forecasting control the quality of machining and diagnosis the technical state of the tool and machine. Methods: cutting theory, oscillation theory, identification and mathematical statistics. System design, was based on the methods of information technology. Experimental data, were analyzed, based on the theory of digital signal processing. To solve the problems prediction used methods of random search.
Kohút, Josef. "Použití měřících sond (nástrojové a obrobkové) pro stroje z produkce TOS KUŘIM." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229879.
Full textRAFANELLI, FRANCESCO. "Analytical plunge milling model and machining sound analysis for chatter forecast and detection." Doctoral thesis, 2016. http://hdl.handle.net/2158/1043310.
Full textBooks on the topic "Machining sound"
Bogue, Ronald. Deleuze and Roxy: The Time of the Intolerable and Godard’s Adieu au langage. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422734.003.0015.
Full textBook chapters on the topic "Machining sound"
Tiainen, Milla. "Sonic Performance and Feminist Posthumanities: Democracy of Resonance and Machinic Sounds." In A Feminist Companion to the Posthumanities, 103–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-62140-1_9.
Full textRubio, Eva. "Machining Process Monitoring System Using Audible Energy Sound Sensors." In Future Manufacturing Systems. Sciyo, 2010. http://dx.doi.org/10.5772/10245.
Full textMaïda, Clara. "Abstract Cartographies and Assemblages of Minimal Sound Units:." In Machinic Assemblages of Desire, 93–106. Universitaire Pers Leuven, 2021. http://dx.doi.org/10.2307/j.ctv1595mb9.9.
Full textCampbell, Iain. "Sounds Flush with the Real:." In Machinic Assemblages of Desire, 107–14. Universitaire Pers Leuven, 2021. http://dx.doi.org/10.2307/j.ctv1595mb9.10.
Full textNeumark, Norie. "Unvoice in Media and the Arts: Voice Going off the Rails." In Voicetracks. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/9780262036139.003.0005.
Full textGardner, Colin. "‘The M ultiplicity is Among Us’: Silence and the M achinic Phylum in Fritz Lang’s M (1931)." In Chaoid Cinema, 22–53. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474494021.003.0002.
Full textConference papers on the topic "Machining sound"
Jarosz, Krzysztof, Yunbo Zhang, and Rui Liu. "Investigating the Role of Auditory Perception of Cutting Process Conditions in CNC Machining." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85804.
Full textLee, Cheng-Hsiung, Jung-Sing Jwo, and Han-Yi Hsieh. "Evaluation of Grinding Wheel Wear Based on Machining Sound and Deep Learning." In 3rd Eurasian Conference on Educational Innovation 2020 (ECEI 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811228001_0084.
Full textTsuboi, Ryo, Kazuyuki Toda, Makoto Yamamoto, Ryuki Nohara, and Dai Kato. "Modelling of Three-Phase Flow in Electro-Chemical Machining." In ASME 2005 Fluids Engineering Division Summer Meeting. ASMEDC, 2005. http://dx.doi.org/10.1115/fedsm2005-77435.
Full textKothuru, Achyuth, Sai Prasad Nooka, Patricia Iglesias Victoria, and Rui Liu. "Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68067.
Full textKothuru, Achyuth, Sai Prasad Nooka, and Rui Liu. "Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6680.
Full textVarghese, Vinay, and Soham Mujumdar. "Effect of Porosity on Tool Wear During Micromachining of Additive Manufactured Titanium Alloy." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-80096.
Full textCampbell, James D. "A Comparison of Fluids Used to Superabrasively Machine a Titanium Alloy." In ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/91-gt-321.
Full textDomazetovska, Simona, Viktor Gavriloski, and Jovana Jovanova. "AI Supported Noise Analyses for Structure Design Requirements Definition." In ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/smasis2021-67961.
Full textChao, Ru-Min, Sie-Yu Li, Chih-Chao Hsu, and Steven Y. Liang. "Characteristic Evaluation of Silicon-Based MEMS Acoustic/Acceleration Sensor." In ASME 2007 International Manufacturing Science and Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/msec2007-31131.
Full textRagavanantham, S., S. Sampathkumar, and S. Santhosh Kumar. "A Study of Temperature Distribution and its Effect on Grinding Wheel Surface During Wheel Loading." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67952.
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