Relevant bibliographies by topics / Machine-tools – Monitoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Machine-tools – Monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Machine-tools – Monitoring"

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Tugengol’d, A. K., V. P. Dimitrov, R. N. Voloshin, and L. V. Borisova. "Monitoring of machine tools." Russian Engineering Research 37, no. 8 (August 2017): 723–27. http://dx.doi.org/10.3103/s1068798x17080196.

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HARRIS, C. G., J. H. WILLIAMS, and A. DAVIES. "Condition monitoring of machine tools." International Journal of Production Research 27, no. 9 (September 1989): 1445–64. http://dx.doi.org/10.1080/00207548908942633.

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MATSUBARA, Atsushi, Motoyuki SUGIHARA, Ahmed A. D. SARHAN, Hidenori SARAIE, Soichi IBARAKI, and Yoshiaki KAKINO. "Research on Spindle and Machining Process Monitoring for Intelligent Machine Tools(Advanced machine tool)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.2 (2005): 469–74. http://dx.doi.org/10.1299/jsmelem.2005.2.469.

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Vijayaraghavan, A., and D. Dornfeld. "Automated energy monitoring of machine tools." CIRP Annals 59, no. 1 (2010): 21–24. http://dx.doi.org/10.1016/j.cirp.2010.03.042.

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Tugengol’d, A. K., V. P. Dimitrov, A. I. Izyumov, and A. R. Yusupov. "Monitoring and control of tools in multifunctional machine tools." Russian Engineering Research 37, no. 5 (May 2017): 440–46. http://dx.doi.org/10.3103/s1068798x17050239.

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Tugengol’d, A. K., R. N. Voloshin, V. P. Dimitrov, L. V. Borisova, and A. R. Yusupov. "Monitoring the Condition of CNC Machine Tools." Russian Engineering Research 40, no. 9 (September 2020): 763–67. http://dx.doi.org/10.3103/s1068798x2009021x.

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Corbett, J. "Smart machine tools." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 212, no. 3 (May 1, 1998): 203–13. http://dx.doi.org/10.1243/0959651981539406.

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Improved manufacturing methods have become crucial factors in retaining global competitiveness for a wide range of products. This has led to the development of new automatic supervision techniques for use in smart machine tools. These are necessary because it is not possible to design and manufacture machine tool structures and systems with work zone areas of sufficient accuracy and repeatability to meet the improved performance requirements demanded by many modern manufacturing companies. The principal areas for automatic supervision of the machine tool are the tooling, appropriate machine elements and the overall machine system. Thermal effects have been shown to be the largest source of dimensional errors and apparent non-repeatability of machines. Therefore, for the highest precision machines on-line temperature monitoring and control is of paramount importance. This paper describes the application of automatic supervision techniques applied to the NION diamond turning and grinding machine, developed by Cranfield Precision Engineering Limited, which is the most accurate machine tool, of its size, currently available, as well as an ultra precision five-axis grinding machine, built in Japan by the Toyoda Machine Works Limited. In addition, following a project at Cranfield University, the benefits of on-line measuring techniques are discussed in obtaining new cost effective manufacturing methods for producing aero-engine turbine blades. Further examples are given, including major projects at Liverpool John Moores University and Eindhoven University of Technology, where significant improvements in accuracy capabilities were obtained for a standard cylindrical grinding machine and a five-axis vertical milling machine.
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Szulewski, Piotr, and Dominika Śniegulska-Grądzka. "Systems of automatic vibration monitoring in machine tools." Mechanik 90, no. 3 (March 6, 2017): 170–75. http://dx.doi.org/10.17814/mechanik.2017.3.37.

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The paper illuminates and discusses some examples of process status monitoring systems in machining. The special techniques based on advanced signals analysis from force sensors, accelerometers, or acoustic emissions are used to detect of chatter vibrations. Monitoring systems could also co-operate with CNC controllers for effective vibration elimination by changing process parameters.
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Mori, M., and M. Fujishima. "Remote Monitoring and Maintenance System for CNC Machine Tools." Procedia CIRP 12 (2013): 7–12. http://dx.doi.org/10.1016/j.procir.2013.09.003.

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Sheng, Zhong Qi, Ze Zhong Liang, Chao Biao Zhang, and Liang Dong. "LabVIEW-Based Wireless Monitoring System of CNC Machine Tools." Applied Mechanics and Materials 121-126 (October 2011): 2075–79. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2075.

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As the development of industry wireless networks, sensor network, innovative sensors, radio frequency identification (RFID), and micro-electro-mechanical system (MEMS) technologies, the industry sector has made great progress in data acquisition, treatment, transfer and analysis, greatly expanded people’s ability to access information, and to control and use them. This paper developed a wireless data acquisition and storage system of CNC machine tools based on LabVIEW graphical programming language and IEEE 802.11 wireless communication protocol, effectively expanded the ability to access and use the CNC machine status information; solved the problem of data collection caused by the environment complexity of manufacture workshop and the hardness of wiring; eliminated the dead zone of manufacture workshop in the processing of the data acquisition of state information of bottom processing equipment; made the bottom machining unit no longer be the information island for manufacturing enterprises.
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Dissertations / Theses on the topic "Machine-tools – Monitoring"

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Van, den Berg Gideon. "Hidden Markov models for tool wear monitoring in turning operations." Diss., Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05302005-114238/.

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Hoh, See Min. "Condition monitoring and fault diagnosis for CNC machine tools." Thesis, Cardiff University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295120.

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Harris, C. G. "Fault diagnosis and condition monitoring for NC/CNC machine tools." Thesis, Cardiff University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381227.

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Marzi, M. Hosein. "An intelligent condition monitoring system with applications to machine tools." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265591.

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Sztendel, Sebastian. "Model referenced condition monitoring of high performance CNC machine tools." Thesis, University of Huddersfield, 2016. http://eprints.hud.ac.uk/id/eprint/34112/.

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Generally, machine tool monitoring is the prediction of the system’s health based on signal acquisition and processing and classification in order to identify the causes of the problem. The producers of machine tools need to pay more attention to their products life cycle because their customers increasingly focus on machine tool reliability and costs. The present study is concerned with the development of a condition monitoring system for high speed Computer Numerical Control (CNC) milling machine tools. A model is a simplification of a real machine to visualize the dynamics of a mechatronic system. This thesis applies recent modelling techniques to represent all parameters which affect the accuracy of a component produced automatically. The control can achieve an accuracy approaching the tolerance restrictions imposed by the machine tool axis repeatability and its operating environment. The motion control system of the CNC machine tool is described and the elements, which compose the axis drives including both the electrical components and the mechanical ones, are analysed and modelled. SIMULINK models have been developed to represent the majority of the dynamic behaviour of the feed drives from the actual CNC machine tool. Various values for the position controller and the load torque have been applied to the motor to show their behaviour. Development of a mechatronic hybrid model for five-axis CNC machine tool using Multi-Body-System (MBS) simulation approach is described. Analysis of CNC machine tool performance under non-cutting conditions is developed. ServoTrace data have been used to validate the Multi-body simulation of tool-to-workpiece position. This thesis aspects the application of state of art sensing methods in the field of condition monitoring of electromechanical systems. The ballscrew-with-nut is perhaps the most prevalent CNC machine subsystem and the condition of each element is crucial to the success of a machining operation. It’s essential to know of the health status of ballscrew, bearings and nut. Acoustic emission analysis of machines has been carried out to determine the deterioration of the ballscrew. Standard practices such as use of a Laser Interferometer have been used to determine the position of the machine tool. A novel machine feed drive condition monitoring system using acoustic emission (AE) signals has been proposed. The AE monitoring techniques investigated can be categorised into traditional AE parameters of energy, event duration and peak amplitude. These events are selected and normalised to estimate remaining life of the machine. This method is shown to be successfully applied for the ballscrew subsystem of an industrial high-speed milling machine. Finally, the successful outcome of the project will contribute to machine tool industry making possible manufacturing of more accurate products with lower costs in shorter time.
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Repo, Jari. "Condition monitoring of machine tools and machining processes using internal sensor signals." Licentiate thesis, KTH, Machine and Process Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-12872.

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Condition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and achieving a more robust machining process. Failures in the machining process and machine tool components may also have negative effects on the final produced part. Instabilities in machining processes also shortens the life time of the cutting edges and machine tool.

The condition monitoring system may utilise information from several sources to facilitate the detection of instabilities in the machining process. To avoid additional complexity to the machining system the use of internal sensors is considered. The focus in this thesis has been to investigate if information related to the machining process can be extracted directly from the internal sensors of the machine tool.

The main contibutions of this work is a further understanding of the direct response from both linear and angular position encoders due the variations in the machining process. The analysis of the response from unbalance testing of turn tables and two types of milling processes, i.e. disc-milling and slot-milling, is presented. It is shown that operational frequencies, such as cutter frequency and tooth-passing frequency, can be extracted from both active and inactive machine axes, but the response from an active machine axis involves a more complex analysis. Various methods for the analysis of the responses in time domain, frequency domain and phase space are presented.


QC 20100518
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Peng, Jie. "Design and analysis of a CNC system for machining and monitoring." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/29730.

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The development of unmanned machining systems has been a recent focus of manufacturing research. Computer Numerical Control (CNC) units, which monitor and control many tightly coupled machining tasks, are inseparable parts of the unmanned manufacturing system. This thesis addresses the detailed design and analysis of a modular CNC system for a milling machine. The feed drive control system of the machine tool is thoroughly studied. The mathematical model for the feed drive control system, which consists of a motion controller, power amplifiers and DC-servo motors, is developed and experimentally verified. A method of estimating cutting forces from current drawn by feed drive motors is developed. The viability of the current sensor as a feeding-force sensing component is illustrated and experimentally tested on the milling machine. Successful application of the current sensor to tool breakage detection in milling operation is presented. The performance of the multi-axis contouring system in precision machining is discussed. The contouring errors induced by feed drive positioning systems in CNC machine tools is investigated using the state space model of the CNC system. The influence of the cutting force on the accuracy of machining is shown to be negligible for feed drives driven by ball leadscrews. Various control strategies for the improvement of machining accuracy are tested in the simulation studies.
Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Guo, Jianying, and 郭建英. "Verification of HKU-DPM results by pollout tests and drillhole logs inweathered tuff." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26663983.

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Onwuka, Goodness Raluchukwu. "Ultra-high precision grinding of BK7 glass." Thesis, Nelson Mandela Metropolitan University, 2016. http://hdl.handle.net/10948/5203.

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With the increase in the application of ultra-precision manufactured parts and the absence of much participation of researchers in ultra-high precision grinding of optical glasses which has a high rate of demand in the industries, it becomes imperative to garner a full understanding of the production of these precision optics using the above-listed technology. Single point inclined axes grinding configuration and Box-Behnken experimental design was developed and applied to the ultra-high precision grinding of BK7 glass. A high sampling acoustic emission monitoring system was implemented to monitor the process. The research tends to monitor the ultra-high precision grinding of BK7 glass using acoustic emission which has proven to be an effective sensing technique to monitor grinding processes. Response surface methodology was adopted to analyze the effect of the interaction between the machining parameters: feed, speed, depth of cut and the generated surface roughness. Furthermore, back propagation Artificial Neural Network was also implemented through careful feature extraction and selection process. The proposed models are aimed at creating a database guide to the ultra-high precision grinding of precision optics.
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Amer, Waseem. "Distributed embedded system with internet GSM connectivity for intelligent e-monitoring of machine tools." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56071/.

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Machining is one of the most important operations in many industrial environments. To prosper in today's competitive industrial world any machining system should be able to deliver the highest possible quality at the lowest possible costs, with very high reliability and flexibility. To fulfil these requirements the idea of e-Monitoring an industrial process was introduced by the Intelligent Process Monitoring and Management (IPMM) Centre at Cardiff University. It has considerable potential applications in industrial systems to not only monitor the health of the machines but also for data management and presentation for future decision making. The research presented in this thesis considers the evolution of two different low complexity signal analysis techniques which can be used for e-Monitoring the health of the cutters used in milling machine tools. The researched techniques are based in the time and frequency domains. The frequency domain analysis technique is based on the idea of using switched capacitor filters and microcontrollers to monitor the frequencies of interest in existing machine tool signals (spindle load and speed) thus avoiding the need for external sensors. The results of frequency domain analysis are used to assess the health of the cutter. The time domain analysis technique uses the same signals to analyse any variations within a tool rotation period and relate these to the health of the cutter. The results are integrated before final decision making which helps in reducing false alarms. The thesis goes on to logically describe the design and development of an on-line microcontroller based distributed intelligent e-Monitoring system for a milling machine tool model Kondia B500, using the proposed signal analysis techniques. Some additional features such as internet and GSM connectivity have also been added to the designed system. The designed system was interfaced to the machine tool and tested for its reliability which was found to be competitive with many other very expensive systems. The designed system can be fitted into a machine tool at the manufacturing stage or it could be interfaced to an existing machine tool for automatically detecting a tooth breakage.
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Books on the topic "Machine-tools – Monitoring"

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Kirchheim, Andreas. Prozessüberwachung bei der Mehrschnittbearbeitung. München: C. Hanser, 1992.

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Vossloh, Martin. Modellgestützte Früherkennung und wissensgestützte Diagnose von Fehlern an Werkzeugmaschinen: Beispielhaft dargestellt an Drehmaschinen. München: C. Hanser, 1988.

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Pachevskiĭ, V. M., and V. I. Korneev. Teorii︠a︡ i praktika mashinostroitelʹnogo oborudovanii︠a︡: Tezisy dokladov mezhdunarodnoĭ nauchno-tekhnicheskoĭ konferent︠s︡ii. Voronezh: Voronezhskiĭ gos. tekhn. universitet, 1996.

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International Conference on Laser Metrology and Machine Performance (1993 Southampton Institute). Laser metrology and machine performance. Edited by Blackshaw D. M. S, Hope A. D, and Smith Graham T. 1947-. Southampton: Computational Mechanics Publications, 1993.

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International Conference on Laser Metrology and Machine Performance (2nd 1995 Southampton, England). Laser metrology and machine performance II. Edited by Hope A. D, Smith Graham T. 1947-, Blackshaw D. M. S, and Southampton Institute (Southampton England). Southampton: Computational Mechanics Publications, 1995.

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International, Conference on Laser Metrology Machine Tool CMM and Robot Performance (6th 2003 University of Huddersfield). Laser metrology and machine performance VI. Southampton: WIT, 2003.

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International Conference on Monitoring and Automatic Supervision in Manufacturing (7th 2004 Zakopane, Poland). Proceedings of the VIIth International Conference on Monitoring and Automatic Supervision in Manufacturing: AC '04, Zakopane, Poland, 19- 21 August 2004. Warsaw: IOS w Krakowie, 2004.

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Crushell, James M. Condition monitoring support tools for CNC machines. Dublin: University College Dublin, 1995.

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Precision Machine Design (Mechanical Engineering Series). CRC, 2008.

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S, Mekid, ed. Introduction to precision machine design and error assessment. Boca Raton, Fla: Taylor & Francis, 2008.

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Book chapters on the topic "Machine-tools – Monitoring"

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Davies, A., and J. H. Williams. "The condition monitoring of machine tools." In Condition Monitoring and Diagnostic Engineering Management, 44–48. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_8.

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Fuertjes, Tobias, Christophe Mozzati, Flavien Peysson, Aitor Alzaga, and Mikel Armendia. "Data Monitoring and Management for Machine Tools." In Twin-Control, 125–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02203-7_7.

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Li, C. Y., and C. R. Zhang. "Real-time model and kinematic control of machine tools." In Condition Monitoring and Diagnostic Engineering Management, 355–60. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_53.

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Kim, Byung Ki, Jae Hyeok Jang, Kyung Woo Hur, Jeong Gun Lee, and Young Woong Ko. "Monitoring and Feedback Tools for Realtime Workloads for Xen Virtual Machine." In Lecture Notes in Electrical Engineering, 151–61. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2911-7_13.

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Wu, Pengcheng, Yan He, Ming K. Lim, Yan Wang, Yulin Wang, and Linming Hu. "A Configurable On-Line Monitoring System Towards Energy Consumption of Machine Tools." In Communications in Computer and Information Science, 139–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2381-2_13.

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Sheng, Zhongqi, Yan Jin, Changchun Liu, and Ying Lu. "Processing State Monitoring of NC Machine Tools Based on BP Neural Network." In Communications in Computer and Information Science, 508–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34240-0_67.

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Yang, Qingqing, and Chunguang Han. "A Routing Algorithm in Wireless Sensor Network for Monitoring System of Machine Tools." In Advances in Intelligent and Soft Computing, 417–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28655-1_66.

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Ning, Qian, Taiyong Wang, Lu Liu, and Yongxiang Jiang. "Research on CNC Machine Tools Integration Condition Monitoring System and Its Information Exchange Technology." In Future Computing, Communication, Control and Management, 441–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27326-1_57.

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Salazar, F., R. Kohler, A. Conde, and F. Landstorfer. "Interpretation of Dam Monitoring Data Combining Visualisation Tools and Machine Learning. Eberlaste Dam Case Study." In Lecture Notes in Civil Engineering, 863–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51085-5_49.

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Yang, Yun, Chao Yin, Xiao-bin Li, and Liang Li. "Multi-source Information Intelligent Collection and Monitoring of CNC Machine Tools Based on Multi-agent." In Challenges and Opportunity with Big Data, 111–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61994-1_11.

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Conference papers on the topic "Machine-tools – Monitoring"

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Wang, Jiahai, and Yanqing Zhang. "Monitoring System of Machine Tools Based on the InTouch." In 2013 International Conference on Mechanical and Automation Engineering (MAEE). IEEE, 2013. http://dx.doi.org/10.1109/maee.2013.27.

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Landi, Luca, Nicola Chiavatti, Luca Grilli, and Marco Preteni. "Configurable Monitoring of Machine Tools Status in Smart Factories." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Singapore: Research Publishing Services, 2020. http://dx.doi.org/10.3850/978-981-14-8593-0_4810-cd.

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Wright, N. P., R. Gan, and C. McVae. "Software and machine learning tools for monitoring railway track switch performance." In 7th IET Conference on Railway Condition Monitoring 2016 (RCM 2016). Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1210.

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Zhou, Zude. "Intelligent monitoring technology for machine tools based on FBG sensing." In 2014 International Conference on Innovative Design and Manufacturing (ICIDM). IEEE, 2014. http://dx.doi.org/10.1109/idam.2014.6912660.

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Deshpande, Amit, and Ron Pieper. "Legacy Machine Monitoring Using Power Signal Analysis." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50019.

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A typical manufacturing job shop comprises of legacy machine tools, new (modern) machine tools, material handling devices, and peripheral manufacturing equipments. Automated monitoring of legacy machine tools has been a long-standing issue for the manufacturing industry primarily because of the computer numeric controller (CNC) closed architecture and limited external communication functionality. This paper describes a non-invasive methodology and development of a software application to monitor real-time machine status, energy usage, and other machining parameters for a legacy machine tool using power signal analysis. State machine algorithm is implemented to detect tool changes and part count. The system architecture, implementation, benefits, limitations, and future work needed for the legacy machine tool monitoring application is explained in detail.
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Rastegari, Ali, Andreas Archenti, and Mohammadsadegh Mobin. "Condition based maintenance of machine tools: Vibration monitoring of spindle units." In 2017 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2017. http://dx.doi.org/10.1109/ram.2017.7889683.

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Kim, Dong-Hoon, Jun-Yeob Song, and Hung-Sun Son. "Real-time monitoring and compensation of chatter vibration in machine tools." In 2011 International Conference on Fluid Power and Mechatronics (FPM). IEEE, 2011. http://dx.doi.org/10.1109/fpm.2011.6045838.

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Ma, Xiaorui, Qian Du, Yu Wang, Jinxu Guo, and Hao Zhang. "Design and implementation of remote monitoring system of numerical control machine tools." In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2017. http://dx.doi.org/10.1109/itnec.2017.8284986.

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"E-LEARNING TOOLS FOR EDUCATION AND TRAINING IN DIAGNOSTICS AND MACHINE CONDITION MONITORING." In 2nd International Conference on Computer Supported Education. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002776903570361.

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Lynn, Roby, Wafa Louhichi, Mahmoud Parto, Ethan Wescoat, and Thomas Kurfess. "Rapidly Deployable MTConnect-Based Machine Tool Monitoring Systems." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-3012.

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The amount of data that can be gathered from a machining process is often misunderstood, and even if these data are collected, they are frequently underutilized. Intelligent uses of data collected from a manufacturing operation can lead to increased productivity and lower costs. While some large-scale manufacturers have developed custom solutions for data collection from their machine tools, small- and medium-size enterprises need efficient and easily deployable methods for data collection and analysis. This paper presents three broad solutions to data collection from machine tools, all of which rely on the open-source and royalty-free MTConnect protocol: the first is a machine monitoring dashboard based on Microsoft Excel; the second is an open source solution using Python and MTConnect; and the third is a cloud-based system using Google Sheets. Time studies are performed on these systems to determine their capability to gather near real-time data from a machining process.
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