Bibliographies thématiques / Machining sound

Littérature scientifique sur le sujet « Machining sound »

Auteur : Grafiati
Publié le 10 mars 2023

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Articles de revues sur le sujet "Machining sound"

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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 (23 février 2022) : 3041–57. http://dx.doi.org/10.1177/09544062211072411.

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Titanium alloys are widely used in various industries such as aerospace, petrochemical, marine, and biomedical due to their high corrosion resistance, high strength, good heat resistance, and lightweight. However, the low thermal conductivity of titanium alloys makes their machinability poor compared to steel alloys. Therefore, it is of great importance to evaluate the machinability of titanium alloys. This experimental study presents the effects of machining parameters on the machinability factors such as surface roughness, power consumption, cutting sound, and machining force in dry turning of Ti-6Al-4V titanium alloy using CBN inserts. Sound emission is a substantial factor for the operator’s safety during the turning process. High cutting sound is generated due to removing a large amount of material from the workpiece using different cutting tools. Since there are not many studies related to the cutting sound in machining processes, the attempt was made to investigate the effect of cutting sound on the machinability factors. The variance analysis results presented the significant effect of cutting speed and cutting depth on power consumption. However, according to the normal plot of the standardized effect, all machining parameters have a high impact on the cutting sound. The highest level of machining parameters, especially cutting depth results in higher cutting sound due to the creation of vibration in the lathe and cutting tool. Besides, the feed rate was found to be the most influential parameter on the surface roughness with a 58.13% contribution. The machining force was affected by both cutting depth and feed rate. The findings revealed that increasing the cutting sound increases the machining force and surface roughness, while the power consumption drops. Based on the desirability function, 0.04 (mm/rev) feed rate, 0.05 (mm) cutting depth, and 60 (m/min) cutting speed were the optimum machining parameters for turning of Ti-6Al-4V titanium alloy.
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AKKUŞ, 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 (30 avril 2021) : 2150072. http://dx.doi.org/10.1142/s0218625x21500724.

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Research on machining continues increasingly today. The effects of independent variables (cutting speed, feed rate, cutting depth) on dependent variables (material removal rate (MRR), average surface roughness (Ra), sound intensity, energy consumption, and vibration) are among the most researched topics in machining. It is also important to achieve optimum results with low cost and time savings in machining. In this study, titanium alloy TC4 material was turned on CNC lathe. Taguchi L[Formula: see text] mixed level design was used in experimental design. MRR, Ra, sound intensity, energy consumption, and vibration values were measured for the determined cutting parameters. The measured values were researched experimentally and statistically. Effective parameters were determined. It was concluded that the cutting parameter that has the greatest effect on MRR, Ra, energy consumption, and vibration is the feed rate. In addition, the depth of cut was the parameter that most affected the sound intensity. Control experiments were carried out after determining the optimum machining parameters. With multiple optimization, the predictions were made with approximately 89% accuracy (92.75% for MRR, 92.49% for Ra, 89.45% for sound intensity, 92.70% for energy consumption, 96.16% for vibration).
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Dong, Quan Cheng, Chang Sheng Ai et Na Wang. « The Study of Tool Wear and Breakage Based on the Characteristic Analysis of Acoustic Spectrum ». Materials Science Forum 532-533 (décembre 2006) : 197–200. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.197.

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Tool monitoring is an important factor to restrict the improvement of production efficiency, machining quality and automation level. The monitoring of the tool wear and breakage conditions on YCM-V116B machining center was studied, and the acquired milling sound signals were analyzed in detail. By means of the classical time-frequency analysis, it was discovered that the wear sound had its own characteristic frequency band, and the frequency component within the frequency band would change according to the change of wear conditions. So that the frequency component within the frequency band will be a good indicator to monitor the tool wear conditions excellently. On the other hand, the tool breakage sound is a random signal that a transient change in amplitude is produced probably when tool breaks. The tool breakage conditions can be detected exactly by the advantages of wavelet decomposition techniques. The analysis implies that the sound generated during the machining process can be used to monitor tool conditions, which provides a new approach to the sound applications in tool monitoring domain.
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Yang, Xuefeng, Xulin Cai, Wenan Yang et Youpeng You. « Novel Tool Path Generation Method for Pocket Machining Using Sound Field Synthesis Theory ». Machines 11, no 2 (18 janvier 2023) : 131. http://dx.doi.org/10.3390/machines11020131.

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Contour parallel tool paths have been proved to be a preferred machining strategy for their advantage of less tool retractions and less sharp turns. The traditional geometrical algorithm-based tool path generation method often makes it hard to simply and simultaneously solve the problems of self-intersection, no residual, and smoothness at the same time due to their contradictions. To address this issue, a contoured parallel tool path generation method for pocket machining is developed in this study. It is based on sound field synthesis theory inspired by the phenomenon of sound wave propagation. Firstly, the simplified medial axis (SMA) tree of the pocket is extracted and the propagation direction of each SMA segment is calculated on account of the geometric characteristics of the pocket boundary. Secondly, the final tool path is obtained through the synthesis of the sound field. Finally, the novel method is verified on five different pockets to generate a contoured parallel milling tool path. After machining these pockets and measuring the machining time, roughness, and cutting force, the experimental results demonstrate that the tool path obtained by the novel method has advantages in improving machining quality and efficiency.
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Sun, Meng Zi, Fei Huang et Hua Jing Li. « The Influence of Structural Parameters on the Sound Absorption Performance of Micro Perforated Absorber ». Applied Mechanics and Materials 701-702 (décembre 2014) : 424–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.424.

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The sound absorption performance of MPA (micro perforated absorber) mainly depends on its structural parameters. Reasonable parameter combination will get a high absorption coefficient. In this paper numerical simulation was used to study sound absorption performance. By this means, the influence of the different parameters on the sound absorption performance are discovered. To verify the result of numerical simulation, MPA samples are fabricated using optimized parameters by laser machining method. The sound absorption performances of these samples are tested and the experimental result is consistent with that of numerical simulation.
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Zajac, Jozef, Zuzana Hutyrová et 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 (juin 2014) : 275–79. http://dx.doi.org/10.4028/www.scientific.net/amr.941-944.275.

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Study provides information about one type of bio-based composite – plastic with wood reinforcement in volume more than 50 % (advantage: renewable, inexpensive, can be used to isolate a sound and have got a low density) and about machining of this unique material. During the machining (turning process was use to produce a surfaces by removing material from a rotating workpiece) were changed two parameters – rotation speed and feed rate (depth of cut was constant). There were observed changes of parameter to surface roughness with change of conditions of machining process.
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Bhandari, Binayak. « Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals ». Micromachines 12, no 12 (29 novembre 2021) : 1484. http://dx.doi.org/10.3390/mi12121484.

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This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer. The classification was performed on the sound and force data generated during machining aluminum sheets for different levels of spindle speed, feed rate, depth of cut, and end-mill diameter, and it was trained on 30 s machining data (10–40 s) of the machining experiments. Since a raw audio waveform is seldom used in DL models, Mel-Spectrogram and Mel Frequency Cepstral Coefficients (MFCCs) audio feature extraction techniques were used in the DL models. The results of DL models were compared for the training–validation accuracy, training epochs, and training parameters of each model. Although the roughness classification by all the DL models was satisfactory (except for CNN with Mel-Spectrogram), the transformer-based modes had the highest training (>96%) and validation accuracies (≈90%). The CNN model with Mel-Spectrogram exhibited the worst training and inference accuracy, which is influenced by limited training data. Confusion matrices were plotted to observe the classification accuracy visually. The confusion matrices showed that the transformer model trained on Mel-Spectrogram and the transformer model trained on MFCCs correctly predicted 366 (or 91.5%) and 371 (or 92.7%) out of 400 test samples. This study also highlights the suitability and superiority of the transformer model for time series sound and force data and over other DL models.
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Hao, Wangs Shen, Xun Sheng Zhu, Biao Jun Tian et M. R. Chi. « Autoregressive Spectrum Analysis of Vibration and Condition Monitoring of Self-Propelled Rotary Tool ». Key Engineering Materials 329 (janvier 2007) : 743–48. http://dx.doi.org/10.4028/www.scientific.net/kem.329.743.

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Self-propelled rotary tools are being rediscovered for their applications in machining of ‘difficult-to-machine’ materials or for general improvement in the productivity of machining operations. The vibration characteristic and the occurrence of chatter during high speed cutting will induce the deterioration of precision and machining surface, tool wear and tool life. This paper deals with the identification of the vibration in SPRT cutting system with AR time series model. The experiment and deduction method provide a sound foundation for improving the structure with high antivibration strength, which could reduce the relative vibration between tool and workpiece in the alloweable scope.
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Miyazaki, T., Y. Tanaka, T. Tokunaga et 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.

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Casal-Guisande, Manuel, Alberto Comesaña-Campos, Alejandro Pereira, José-Benito Bouza-Rodríguez et Jorge Cerqueiro-Pequeño. « A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring ». Mathematics 10, no 3 (6 février 2022) : 520. http://dx.doi.org/10.3390/math10030520.

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The workers operating and supervising machining tools are often in charge of monitoring a high number of parameters of the machining process, and they usually make use of, among others, cutting sound signals, for following-up and assessing that process. The interpretation of those signals is closely related to the operational conditions of the machine and to the work environment itself, because such signals are sensitive to changes in the process’ input parameters. Additionally, they could be considered as a valid indicator for detecting working conditions that either negatively affect the tools’ lifespan, or might even put the machine operators themselves at risk. In light of those circumstances, this work deals with the proposal and conceptual development of a new methodology for monitoring the work conditions of machining tools, based on expert systems that incorporate a reinforcement strategy into their knowledge base. By means of the combination of sound-processing techniques, together with the use of fuzzy-logic inference engines and hierarchization methods based on vague fuzzy numbers, it will be possible to determine existing undesirable behaviors in the machining tools, thus reducing errors, accidents and harmful failures, with consequent savings in time and costs. Aiming to show the potential for the use of this methodology, a concept test has been developed, implemented in the form of a short case study. The results obtained, even if they require more extensive validation, suggest that the methodology would allow for improving the performance and operation of machining tools, as well as the ergonomic conditions of the workplace.
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Thèses sur le sujet "Machining sound"

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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.

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The object of this work is to apply the measuring probe in the process of the machining and explore the use of the measuring probes. For the practical of the measurement was use measuring probes, the program supplied with the measuring probes, machining centers and the resulting data were evaluated on a PC. Finally, work is to evaluate the measurements and describe the appropriateness of the application.
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Нагорний, Володимир В`ячеславович, Владимир Вячеславович Нагорный et 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.

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Робота присвячена актуальній проблемі розвитку методів контролю динамічної поведінки металообробних технологічних систем та розробки методу визначення їх ресурсу. У роботі вперше поставлене і вирішене науково-технічне завдання визначення ресурсу обробної системи безпосередньо в процесі її роботи в умовах, коли «Норми…» , яки регламентують ступінь критичності динамічної поведінки для даних систем, відсутні. Це завдання вирішене за рахунок того, що розроблена у роботі прогнозна модель вигідно відрізняється від прийнятих тим, що ресурс, який шукається, включено до її математичної структури і визначається в процесі ідентифікації моделі за результатами моніторингу тренда звуку, супроводжуючого роботу обробних систем. Це дозволило визначати індивідуальний ресурс обробної системи, який відповідає даним технологічним умовам її експлуатації. Для здійснення оперативного контролю динамічної поведінки обробної системи був розроблений алгоритм контролю та його програмна реалізація, яка за допомогою мікропроцесорного пристрою дозволила автоматизувати процес контролю динамічної поведінки обробної системи та визначення її ресурсу. Методи досліджень: теорії коливань, ідентифікації та математичної статистики. Розроблення алгоритму контролю здійснювалося на основі методів інформаційних технологій. Аналіз експериментальних даних проводився на основі теорії цифрової обробки сигналів, для вирішення завдань визначення ресурсу використані методи випадкового пошуку.
Работа 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.
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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.

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The purpose of this dissertation is to familiarize the reader with basic recognition of machine measuring probes for tools and workpieces. Namely then in area of measurement on CNC machining centers. Both recognition from aspects of principle of function of sensing elements and type of transmition to control system, and recognition from constructional aspects are described here. Further it puts mind on equipment of machine probes (touches etc.) and rules for best selection of this equipment. Next part of this work describes selection of appropriate probes, for universal machines from production of firm TOS Kuřim. Last part deals with testing of designed measuring system for concrete machine from production of firm TOS Kuřim. His typical composition, evaluation of tests results and achieved accuracy is also described here.
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RAFANELLI, FRANCESCO. « Analytical plunge milling model and machining sound analysis for chatter forecast and detection ». Doctoral thesis, 2016. http://hdl.handle.net/2158/1043310.

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The main aim of the activity is to develop models and methods for chatter avoidance and control. An analytical plunge milling model will be presented and described, starting from the few literature existing papers. New method for cutting coefficients measurement is introduced, as well together with a more accurate configuration description and the solution algorithm in order to achieve the Stability Lobes Diagrams. The second part of this thesis is focused on developing an active control logic, to be used in a general milling application and able to recognize the milling phase (example air cutting, stable or unstable cut) based on acoustical analysis.
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Livres sur le sujet "Machining sound"

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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.

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Focusing specifically on Jean-Luc Godard’s experimental 3-D outing, Adieu au langage (2014), this chapter equates becoming-animal (manifested through Godard’s dog, Roxy) with the time of the intolerable, a crisis in the action-image which precludes any absorption of affect within the sensory-motor schema. In this sense, Roxy becomes an immanent, animistic force that remakes the world (and by extension, as in all of Godard’s films, cinema itself) into a protean force of becoming, where even 3-D bifurcates and deterritorialises as a new form of machinic fabulation. In the case of the intolerable, where our sensory-motor schema is allowed to jam or break, a different type of image appears, what Deleuze calls a pure optical-sound image: ‘opsigns’ and ‘sonsigns’, images where the seen and heard no longer extend into action. In Adieu au langage, Godard’s use of 3D also offers a new kind of time-image, a lectosign (a visual image which must be ‘read’ as much as seen) that requires the eye’s negotiation of conflicting points of attention within the shot, thereby inducing crisis and the intolerable within the image itself.
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Chapitres de livres sur le sujet "Machining sound"

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Tiainen, Milla. « Sonic Performance and Feminist Posthumanities : Democracy of Resonance and Machinic Sounds ». Dans 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.

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Rubio, Eva. « Machining Process Monitoring System Using Audible Energy Sound Sensors ». Dans Future Manufacturing Systems. Sciyo, 2010. http://dx.doi.org/10.5772/10245.

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Maïda, Clara. « Abstract Cartographies and Assemblages of Minimal Sound Units : ». Dans Machinic Assemblages of Desire, 93–106. Universitaire Pers Leuven, 2021. http://dx.doi.org/10.2307/j.ctv1595mb9.9.

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Campbell, Iain. « Sounds Flush with the Real : ». Dans Machinic Assemblages of Desire, 107–14. Universitaire Pers Leuven, 2021. http://dx.doi.org/10.2307/j.ctv1595mb9.10.

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Neumark, Norie. « Unvoice in Media and the Arts : Voice Going off the Rails ». Dans Voicetracks. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/9780262036139.003.0005.

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This chapter introduces the figure unvoice to listen to works where the usual relationship between soundtrack and image track is disturbed and disturbing. As unvoicing breaks the usual synchronization and hierarchy of image over sound, their very relationship becomes audible. The chapter works with the figures of ‘un,’ medianatures, minor aesthetics, and ekphrasis, bringing them into conversation with works ranging from YouTube lip-syncing remixes to artful animations, from machinima to cat videos, from video artworks to installations. It encounters unvoicing in works of portraiture and subtitling. Whether accidental or purposeful, unvoicing thinks the relations of voice, soundtrack and imagetrack anew.
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Gardner, Colin. « ‘The M ultiplicity is Among Us’ : Silence and the M achinic Phylum in Fritz Lang’s M (1931) ». Dans Chaoid Cinema, 22–53. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474494021.003.0002.

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The book opens with an analysis of M, Fritz Lang’s first sound film, starring Peter Lorre as the child murderer, Hans Beckert. The film borrows a lot of the techniques that were popular in German radio at that time –the sound of gongs to introduce new scenes or programmes; newspaper headlines read aloud as diegetic exposition – and it also lacks a musical score (a staple of so-called silent cinema during actual screenings). The film’s key ideological underpinning is the World War I veteran Ernst Jünger’s application of the war ego, based on the dictum, ‘kill or be killed,’ to the peacetime ego of ‘thou shalt not kill’ whereby the former overrides the latter in a mechanized society driven by what he called ‘second consciousness’ and what Michel Foucault would subsequently call the dispositif. The film’s silences represent what Deleuze and Guattari call the ‘machinic phylum,’ a vector of forces that links different points as pre-subjective singularities but also brings them together as aggregates/totalities. Thus the phylum represents Beckert’s untrammeled desires that defy the dispositif (largely because they are unrepresentable except as an internal snuff film that can only be halted in the actual act of killing).
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Actes de conférences sur le sujet "Machining sound"

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Jarosz, Krzysztof, Yunbo Zhang et Rui Liu. « Investigating the Role of Auditory Perception of Cutting Process Conditions in CNC Machining ». Dans ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85804.

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Abstract In the era of Industry 4.0, the machining sound has been extensively adopted in tool condition monitoring systems, virtual machining environment, and remote machining solutions. However, only limited attention has been paid to understand how experienced machinists detect tool wear and improper cutting conditions based on their hearing in the real machining environment. This paper aims to experimentally investigate and analyze the auditory perception of CNC operators during the cutting process and their capabilities of detecting unfavorable cutting conditions and faults using their sense of hearing and expertise. The sound in the machining environment was analyzed in the aspect of sound pressure levels (SPL). Optimal positions for sound sample acquisition were determined and audio data was recorded for future analysis. Experimental cutting tests with simulated process faults were conducted, where machinists with varying degrees of experience observed the process, listened to the machining sound and tried to determine whether cutting conditions were normal or if faults occurred. The primary research goal was to analyze how well operators can monitor the process using their various senses and to investigate the role of sound and auditory perceptions of trained professionals in cutting process supervision and monitoring. SPL measurements have shown that the sound pressure varies substantially in the machining environment, which is expected to affect the quality and volume of recorded machining sound depending on microphone positioning. Cutting tests have shown that the machinists use various senses to determine faults in the process, relying most significantly on auditory stimuli, with other factors, such as vibrations or visual examination of the workpiece having a secondary effect in the assessment of cutting process conditions and outcomes.
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Lee, Cheng-Hsiung, Jung-Sing Jwo et Han-Yi Hsieh. « Evaluation of Grinding Wheel Wear Based on Machining Sound and Deep Learning ». Dans 3rd Eurasian Conference on Educational Innovation 2020 (ECEI 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811228001_0084.

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Tsuboi, Ryo, Kazuyuki Toda, Makoto Yamamoto, Ryuki Nohara et Dai Kato. « Modelling of Three-Phase Flow in Electro-Chemical Machining ». Dans ASME 2005 Fluids Engineering Division Summer Meeting. ASMEDC, 2005. http://dx.doi.org/10.1115/fedsm2005-77435.

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Electro-Chemical Machining (ECM) is an advanced machining technology. It has been applied to highly specialized fields such as aerospace, aeronautics and medical industries. However, it still has some problems to be overcome. The efficient tool-design, electrolyte processing, and disposal of metal hydroxide sludge are the typical issues. To solve such problems, CFD is considered to be a powerful tool in the near future. However, the numerical method that can satisfactorily predict the flow has not been established because of the complex flow natures. In the present study, we investigate the modelling of the three-phase flow (i.e. fluid, hydrogen bubble and metal sludge) in ECM process. First, the developed code is applied to the two-dimensional channel configuration. The interactions among three-phases and the dissolved wall are simulated, to verify the modelling and to determine the model parameters, Next, the sinusoidal channel is machined by our code. It is confirmed that hydrogen bubbles in the separation region suppress the dissolution of the wall, and make the final wall shape be wavy. Through this study, it is exhibited that our developed model and code are sound and useful for simulating ECM process.
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Kothuru, Achyuth, Sai Prasad Nooka, Patricia Iglesias Victoria et Rui Liu. « Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques ». Dans 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.

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The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.
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Kothuru, Achyuth, Sai Prasad Nooka et Rui Liu. « Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks ». Dans ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6680.

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Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.
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Varghese, Vinay, et Soham Mujumdar. « Effect of Porosity on Tool Wear During Micromachining of Additive Manufactured Titanium Alloy ». Dans ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-80096.

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Abstract Porosity is a major quality issue in additively manufactured (AM) materials due to improper selection of raw material or process parameters. While porosity is kept to a minimum for structural applications, parts with intentional (engineered) porosity find applications in prosthetics, sound dampeners & mufflers, catalytic converters, electrodes, heat exchangers, filters, etc. During post-processing of additive manufactured components using secondary machining to obtain required dimensional tolerance and/or surface quality, part porosity could lead to fluctuating cutting forces and reduced tool life. The machinability of the porous AM material is poor compared to the homogenous wrought material due to the intermittent cutting and anisotropy of AM materials. The cutting parameters for machining are generally optimized for continuous wrought material and are not applicable for porous AM material. Micromilling experiments were carried out on AM Ti6Al4V alloy with different porosity levels and cutting speed using a 1 mm diameter end mill. The progression of tool wear and associated mechanisms during micro-milling of additive manufactured Ti6Al4V samples with different porosity levels are experimentally investigated. Insights into tool-workpiece interaction during micro-machining are obtained in cases where pore size could be comparable to the cutting tool diameter. This research could lead to efficient hybrid additive-subtractive manufacturing technologies with improved tool life and reduced costs.
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Campbell, James D. « A Comparison of Fluids Used to Superabrasively Machine a Titanium Alloy ». Dans 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.

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The objective of this paper was to compare the creep feed superabrasive machining of an alpha-beta structural titanium alloy, using a water-soluble and a straight oil grinding fluid, in terms of residual stress, specific energy, power flux and microstructure. The statistical effect of process variables on these criteria was investigated using a Taguchi screening design of experiment. Grinding wheel peripheral velocity, abrasive size and fluid type were the most important factors contributing to compressive residual stress. After the depth of cut, fluid type contributed the most variation to specific energy and power flux. Both fluids produced testpieces that were microstructurally sound, and were essentially stress free or had favorable compressive residual stress.
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Domazetovska, Simona, Viktor Gavriloski et Jovana Jovanova. « AI Supported Noise Analyses for Structure Design Requirements Definition ». Dans 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.

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Abstract The artificial intelligence (AI) field has encountered a turning point mainly due to advancements in machine learning, which allows systems to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and productivity rates and to optimize design and process parameters. The systems for acoustic event detection and classification (AED/C) of noise events is a process consisted of feature extraction of the signals, meaning processing acoustic signals and converting them into symbolic descriptions that correspond to the various sound events present in the signals and their sources. The main objective of the AED/C systems is to develop algorithms able to recognize and classify sound events that occur in the chosen environment, giving an appropriate response to users. By utilizing the acoustic events detection and classification systems, a clear set of design requirements can be extracted based on the noise to be attenuated. A smart structure design for noise attenuation needs clear noise input for proper smart material choice, placement and active control. This paper shows a method for detection of noise events based on machine-learning algorithm that can be further used for definition of design requirements.
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Chao, Ru-Min, Sie-Yu Li, Chih-Chao Hsu et Steven Y. Liang. « Characteristic Evaluation of Silicon-Based MEMS Acoustic/Acceleration Sensor ». Dans ASME 2007 International Manufacturing Science and Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/msec2007-31131.

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Due to its smallness in size and economic-saving in batch fabrication, the acoustic micro sensor shows many potential applications in the field of manufacturing and machining monitoring system if it can help to pick up information of sound and vibration signals at specific range. In this paper, brief discussion for the design of the micro-sensor and its fabrication issue will be made. Fundamental frequency testing results by the piezo-electric shaker and acoustic measurement within the water tank are given. A 6-inch silicon wafer fabrication process and sensor design having resonant frequency in the range of 20k–60k Hz are presented and discussed. By selecting appropriate thickness of the structure layer on a SOI wafer, it is possible to customer-make a micro-sensor to sense within a given-range of interest and to apply it in a manufacturing monitoring system.
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Ragavanantham, S., S. Sampathkumar et S. Santhosh Kumar. « A Study of Temperature Distribution and its Effect on Grinding Wheel Surface During Wheel Loading ». Dans 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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The cutting ability of abrasive grains is considerably changed by wheel loading and leads to excessive rubbing at the wheel-work interface. Therefore, the wheel life, the overall performance of the grinding operation and the surface finish of the work piece are directly having an effect by the amount of loading over the wheel periphery. Wheel loading can, thus, be considered as an important factor for evaluating the grinding process. Though, few monitoring systems based on ultra sound and acoustic emissions are on the research anvil, they demand high investment. Monitoring of grinding wheel loading, using IR (Infra Red) which is affordable and cost effective for small scale industries is the scope of the present work. This work involves study on the temperature on cutting surface of grinding wheel during machining process. This information helps to understand the influence of temperature on wheel loading. In this respect, the experiments were carried out with Aluminium Oxide (Al2O3) and Silicon Carbide (SiC) grinding wheels over High carbon High chromium steel (HCHCr) and Mild steel specimens. The grinding wheels and specimens were chosen for pilot study, as they are widely used in Indian small scale Industries. The experiments were carried out with (WC) and without (WOC) general purpose soluble oil coolant. Temperature measurements were recorded at two locations on the grinding wheel to understand the distribution. The results show that the temperature rise during grinding was insignificant to cause any influence on the wheel loading.
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