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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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7

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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9

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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10

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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11

Císar, Miroslav, Ivan Kuric, Nadežda Čuboňová, and Štefan Vlček. "PROPOSAL OF DEVICE FOR MONITORING OF EDUCATIONAL MACHINE TOOLS." Advances in Science and Technology Research Journal 10, no. 31 (2016): 80–86. http://dx.doi.org/10.12913/22998624/64067.

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12

Ciaburro, Giuseppe, Gino Iannace, Virginia Puyana-Romero, and Amelia Trematerra. "Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring." Applied Sciences 11, no. 14 (July 14, 2021): 6488. http://dx.doi.org/10.3390/app11146488.

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The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.

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13

Jing, Lu Yang, Tai Yong Wang, Dong Xiang Chen, and Jing Xiang Fang. "Design and Implementation of Online Monitoring and Remote Diagnostic System for CNC Machine Tools." Advanced Materials Research 819 (September 2013): 136–39. http://dx.doi.org/10.4028/www.scientific.net/amr.819.136.

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With the development of network technology and fault diagnosis technology, monitoring and diagnosis methods for the CNC machine tools had a great change. In this paper, an online monitoring and remote diagnosis system for CNC machine tools was built. The system was consisted of the multi-channel online acquisition system and remote fault diagnosis system. The online acquisition system achieved a real-time monitoring for CNC machine tools. The remote fault diagnosis system provided the management of devices and assistant for experts to analyze data which was uploaded from acquisition system. The system offered real-time state information of CNC machine tools and reduced downtime of machine effectively.

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14

Shi, Rong Bo, Zhi Ping Guo, and Zhi Yong Song. "Research of On-Line Monitoring Technology of Machining Accuracy of CNC Machine Tools." Advanced Materials Research 846-847 (November 2013): 268–73. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.268.

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This paper analyzes CNC machine tools machining error sources, put forward a kind of on-line monitoring technology of CNC machine tools machining accuracy based on online neural network. Through the establishment of CNC machine tools condition monitoring platform, collection sensor signal of the key components of CNC machine tools, using time domain and frequency domain method of the original signal processing, extract the characteristic related to machining accuracy change, input to the neural network, identification the changes of machining accuracy. The experimental results show that, the on-line monitoring technology based on neural network, can identify the changes of machining accuracy.

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15

FUJISHIMA, Makoto, Masahiko MORI, Koichiro NARIMATSU, and Naruhiro IRINO. "UTILISATION OF IOT AND SENSING FOR MACHINE TOOLS." Journal of Machine Engineering 19, no. 1 (February 20, 2019): 38–47. http://dx.doi.org/10.5604/01.3001.0013.0447.

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Strong requirements for automation in the production processes using machine tools have been increasing due to lack of high-skilled machining engineers. Automation used to be utilised in mass production, but it is also necessary in medium- to low-volume production recently. Next requirements will be monitoring or sensing functions to make the following possible: prompt service when the machine stops; detection of abnormality before the machine breaks down; and compensation of thermal displacement to ensure machining accuracy. These now need to be performed automatically in place of operators so that abnormality can be detected during machining operation. In this paper core technologies to support automation system will be discussed which are operation monitoring, predictive maintenance, sensing interface and thermal displacement compensation as a sensing application.

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16

Aoyama, Tojiro. "Mini Special Issue on Machining Control and Process Monitoring." International Journal of Automation Technology 8, no. 6 (November 5, 2014): 791. http://dx.doi.org/10.20965/ijat.2014.p0791.

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Control and process monitoring are key technologies supporting high machining accuracy and efficiency. This special issue features six papers taking novel approaches to controlling machine and cutting tools and monitoring the machining process. The motion control of machine tools and cutting tools are introduced. A new challenge for monitoring the machining process by referring to NC control servo signals implements a practical proposal. The precise identification of friction at driving elements of machine tool components is an important factor in improving machine tool control motion accuracy. I would like to express my sincere appreciation to the authors and reviewers whose invaluable efforts have helped make the publication of this manuscript possible.

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17

Han, Xing Guo, and Bin Wu Wang. "Research on Distributed Remote Monitoring System for NC Machine Tools." Applied Mechanics and Materials 190-191 (July 2012): 786–89. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.786.

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A whole design scheme of distributed remote monitoring system of NC machine tools is put forward and the middleware technology of combination of CORBA and web service is importantly discussed in this paper.This system is divided into three layers—field layer,middle layer and user layer. Finally, in this system platform, the errors of circular interpolation and linear interpolation are measured.

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18

Tedeschi, Stefano, Jörn Mehnen, Nikolaos Tapoglou, and Roy Rajkumar. "Security Aspects in Cloud Based Condition Monitoring of Machine Tools." Procedia CIRP 38 (2015): 47–52. http://dx.doi.org/10.1016/j.procir.2015.07.046.

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19

Hui, Mingxin, Jing Wang, Bin Liu, Xun Wang, Xiaobin Cheng, and Jun Yang. "The cutting parameters dependent vibration monitoring method for machine tools." Journal of the Acoustical Society of America 148, no. 4 (October 2020): 2793. http://dx.doi.org/10.1121/1.5147774.

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20

Matsubara, Atsushi. "707 Monitoring Technique for High Speed Spindle of Machine Tools." Proceedings of Conference of Kansai Branch 2009.84 (2009): _7–7_. http://dx.doi.org/10.1299/jsmekansai.2009.84._7-7_.

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21

Mori, M., M. Fujishima, M. Komatsu, Bingyan Zhao, and Yadong Liu. "Development of remote monitoring and maintenance system for machine tools." CIRP Annals 57, no. 1 (2008): 433–36. http://dx.doi.org/10.1016/j.cirp.2008.03.108.

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22

Behrendt, Thomas, André Zein, and Sangkee Min. "Development of an energy consumption monitoring procedure for machine tools." CIRP Annals 61, no. 1 (2012): 43–46. http://dx.doi.org/10.1016/j.cirp.2012.03.103.

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23

Bartal, P., and L. Monostori. "A pattern recognition based vibration monitoring module for machine tools." Robotics and Computer-Integrated Manufacturing 4, no. 3-4 (January 1988): 465–69. http://dx.doi.org/10.1016/0736-5845(88)90018-x.

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24

Xing, Kanglin, Xiaojie Liu, Zhaoheng Liu, J. R. R. Mayer, and Sofiane Achiche. "Low-Cost Precision Monitoring System of Machine Tools for SMEs." Procedia CIRP 96 (2021): 347–52. http://dx.doi.org/10.1016/j.procir.2021.01.098.

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25

Guo, Yuan, Yu Sun, and Kai Wu. "Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988141989801. http://dx.doi.org/10.1177/1729881419898017.

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Intelligent manufacturing as the development direction of the new generation manufacturing system has become a hot research topic. Computer numerical control (CNC) machine tools are the core manufacturing equipment in discrete manufacturing enterprises, collecting and monitoring the data is an important part of intelligent manufacturing workshops. It has a great significance to improve the production efficiency of enterprises and eliminate information islands. The purpose of this article is to solve the problems of data acquisition and monitoring of CNC machine tools in the manufacturing workshop of enterprises. This article uses FOCAS data acquisition method to research and develop the data acquisition and monitoring system of CNC machine tools in intelligent manufacturing workshop. The research results show that the equipment information model based on MTConnect protocol and FOCAS can solve the data acquisition and storage functions of CNC machine tools well. Using the object-oriented Petri net model, it can solve various uncertain factors in numerical control (NC) machining tasks and realize the monitoring function of CNC machining tasks in the workshop. Based on the NC program analysis, the calculation method of machining time in the NC program can determine the preventive maintenance cycle of the machine based on the machine fault information. Based on VS2013 development environment, Qt application framework and SQL Server 2012 database, the numerical control machine tool data acquisition and monitoring prototype system was developed, and the system was verified in the workshop to prove the effectiveness of the system.

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26

Hussein, Eslam A., Christopher Thron, Mehrdad Ghaziasgar, Antoine Bagula, and Mattia Vaccari. "Groundwater Prediction Using Machine-Learning Tools." Algorithms 13, no. 11 (November 17, 2020): 300. http://dx.doi.org/10.3390/a13110300.

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Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features.

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27

Huang, Min, Xiu Li Liu, and Le Yan. "Study on High-Grade CNC Machine Tool Wear Monitoring Methods and Experimental System." Advanced Materials Research 591-593 (November 2012): 1844–48. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1844.

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Today, CNC machine tools are moving in the high-speed, high precision, heavy and complex processing of direction, leading to early failure in service due to machine performance, if not timely diagnosis and early warning, will result in waste increases, fluctuations in the quality, productivity decline.Therefore, to ensure reliable operation of CNC machine tools is very important.To build fault diagnosis of CNC machine tools and get test method as the goal, the tool wear experiments are carried out. Signals for cutting tool with different wear in milling process are detected，acquisited and analyzed through vibration sensors and acoustic emission sensors on the milling tools. To LabVIEW8.6 as development platform, a fault diagnosis experimental system of CNC machine tools is developed, including data acquisition module, signal analysis module, fault diagnosis module.

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28

Sossenheimer, Johannes, Jessica Walther, Jan Fleddermann, and Eberhard Abele. "A Sensor Reduced Machine Learning Approach for Condition-based Energy Monitoring for Machine Tools." Procedia CIRP 81 (2019): 570–75. http://dx.doi.org/10.1016/j.procir.2019.03.157.

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29

Sheng, Zhong Qi, Liang Dong, and Chang Ping Tang. "Research on Monitoring of Machine Tools Based on Wireless Sensor Network." Advanced Materials Research 230-232 (May 2011): 616–19. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.616.

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This paper discusses the structure of wireless sensor network (WSN) and the key technologies for the monitoring of machine tools. Multi-sensor is used to monitor the acoustic emission and vibration signal during the manufacturing process of machine tools. Vibration signals and acoustic characteristics are extracted by using wavelet analysis. Based on the fusion of BP artificial neural networks and multi-sensor information, the monitoring of machine tool is carried out in the environment of wireless sensor network.

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30

Chu, Y. C., T. N. Pham, F. R. Hsu, M. J. Tuw, C. W. Tan, M. C. Chay, S. C. Lim, and M. F. Tsai. "An effective method for monitoring the vibration data of bearings to diagnose and minimize defects." MATEC Web of Conferences 189 (2018): 03019. http://dx.doi.org/10.1051/matecconf/201818903019.

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Monitoring of vibration in machine tools is becoming a very important application in industry to reduce machine failures, maintenance costs, and dead time. In this paper, we propose a method to identify possible faults based on vibration data from which predictions about the working condition of the machine tools can be made. We used an accelerometer to collect the vibration data from which to analyse the health of machine tools by diagnosing whether they are in good or faulty condition for working. In our experiments, we introduced a machine called the Reliance Electric motor, which has a bearing running inside it. Our research analyses vibration data from components of the bearing including the outer bearing, inner bearing, and rolling element. The experimental results show that our method is highly accurate in diagnosing failures and significantly reduces the maintenance costs of machine tools.

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31

Huang, Chih-Yung, and Jian-Hao Chen. "Development of Dual-Axis MEMS Accelerometers for Machine Tools Vibration Monitoring." Applied Sciences 6, no. 7 (July 12, 2016): 201. http://dx.doi.org/10.3390/app6070201.

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32

Denkena, Berend, Kai Martin Litwinski, and Haythem Boujnah. "Process Monitoring with a Force Sensitive Axis-slide for Machine Tools." Procedia Technology 15 (2014): 416–23. http://dx.doi.org/10.1016/j.protcy.2014.09.096.

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33

Józwik, Jerzy, Andrzej Wac-Włodarczyk, Joanna Michałowska, and Eng Monika Kłoczko. "Monitoring of the Noise Emitted by Machine Tools in Industrial Conditions." Journal of Ecological Engineering 19, no. 1 (January 1, 2018): 83–93. http://dx.doi.org/10.12911/22998993/79447.

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34

Hu, Shaohua, Fei Liu, Yan He, and Tong Hu. "An on-line approach for energy efficiency monitoring of machine tools." Journal of Cleaner Production 27 (May 2012): 133–40. http://dx.doi.org/10.1016/j.jclepro.2012.01.013.

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35

Goyal, Deepam, and B. S. Pabla. "Development of non-contact structural health monitoring system for machine tools." Journal of Applied Research and Technology 14, no. 4 (August 2016): 245–58. http://dx.doi.org/10.1016/j.jart.2016.06.003.

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36

Shi, Rong Bo, Zhi Ping Guo, and Zhi Yong Song. "Research Based on State Monitoring of CNC Machine Tools Intelligent Security System." Applied Mechanics and Materials 427-429 (September 2013): 1328–32. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1328.

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Analyse the cause of fault in CNC Machine, research the corresponding solve scheme, and to realize the state can monitor equipment operation, improve equipment reliability, the development set of machine condition monitoring, fault warning, fault diagnosis and troubleshooting as one of the intelligent security system. Based on the CNC machine intelligence support system research, design, introduces the key technologies and methods. Screw lift state of motion monitoring, for example, trend analysis exercise state, intelligent fault diagnosis, in order to achieve protection of the intelligent CNC machine tools to verify the practicality of intelligent security systems.

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37

Brecher, Christian, Tiandong Xi, Igor Medeiros Benincá, Sebastian Kehne, and Marcel Fey. "Intelligente Überwachung von Werkzeugverschleiß/Intelligent monitoring of tool wear based on machine internal data." wt Werkstattstechnik online 111, no. 05 (2021): 309–13. http://dx.doi.org/10.37544/1436-4980-2021-05-43.

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Numerische Steuerungen für Werkzeugmaschinen erfassen eine erhebliche Menge an Sensordaten für die Achsregelung. Diese liefern nicht nur Informationen über die aktuellen Achspositionen oder die Ströme, sondern können mithilfe von Modellen auch für das Monitoring von anderen Prozessgrößen verwendet werden. In diesem Beitrag wird ein Machine-Learning-Verfahren zur Überwachung von Werkzeugverschleiß untersucht, welches allein auf maschinen-internen Daten basiert.   Numerical controls for machine tools acquire a considerable amount of sensor data for axis control. This information, such as the current axis position or the motor currents, can be used for monitoring other process variables with the aid of models. This article investigates a machine learning method for monitoring tool wear in machine tools, based on machine-internal data only.

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38

Chan, Tzu-Chi, Ze-Kai Jian, and Yu-Chuan Wang. "Study on the Digital Intelligent Diagnosis of Miniature Machine Tools." Applied Sciences 11, no. 18 (September 9, 2021): 8372. http://dx.doi.org/10.3390/app11188372.

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Several industries are currently focusing on smart technologies, high customization, and the integration of solutions. This study focuses on the intelligent diagnosis of digital small machine tools. Furthermore, the main technology processes and cases for smart manufacturing for machine tool applications are introduced. Owing to the requirements of automated processing to determine the quality of a process in advance, the health status of a machine should be monitored in real time, and machine abnormalities should be detected periodically. In this study, we captured the real-time signals of temperature, spindle current, and the vibration of three small five-axis machine tools. Moreover, we used a principal component analysis to diagnose and compare the health status of the spindles and machines. We developed a miniature machine tool health monitoring application to avoid time delays and loss from damage, and used the application to monitor the machine health online under an actual application. Therefore, the technology can also be used in an online diagnosis of machine tools through modeling technology, allowing the user to monitor trends in the machine health. This research provides a feasible method for monitoring machine health. We believe that the intelligent functions of machine tools will continue to increase in the future.

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39

ZHANG, JULIE, and HONG NIE. "EXPERIMENTAL STUDY AND LOGISTIC REGRESSION MODELING FOR MACHINE CONDITION MONITORING THROUGH MICROCONTROLLER-BASED DATA ACQUISITION SYSTEM." Journal of Advanced Manufacturing Systems 08, no. 02 (December 2009): 177–92. http://dx.doi.org/10.1142/s0219686709001742.

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Machine condition monitoring plays an important role in machining performance. A machine condition monitoring system will provide significant economic benefits when applied to machine tools and machining processes. By applying Taguchi design method, real-time pilot experimental study was conducted on a CNC machining center for monitoring the end mill cutting operations through the vibration data collection via a microcontroller-based data acquisition system. Featured machining signals were identified through data analyses and regression models were established that incorporates different combinations of featured machining signals and machining parameters in using logistic regression modeling approach. The onsite tests show that the developed logistic models including the featured machining signals can correctly distinguish worn and new cutting tools. Therefore, they can help construct decision-making mechanism for machine condition monitoring.

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40

Takei, Masaya, Daisuke Kurihara, Seiichiro Katsura, and Yasuhiro Kakinuma. "Hybrid Control for Machine Tool Table Applying Sensorless Cutting Force Monitoring." International Journal of Automation Technology 5, no. 4 (July 5, 2011): 587–93. http://dx.doi.org/10.20965/ijat.2011.p0587.

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Accurate monitoring and adaptive control to maintain optimum machining conditions are required for intelligent machine tools. Methods using additional sensors have been studied to add these functions to machine tools; however, incorporating additional sensors leads to higher production costs and reductions in mechanical stiffness. In the present study, a sensorless method of monitoring cutting force that utilizes the current signal and the second-order derivative of position is developed. Moreover, applying sensorless cutting force monitoring, a hybrid control method is proposed in which the sensorless monitoring is used to simultaneously control both the position trajectory and cutting force in the feed direction.

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41

Yu, Guang Lin, and Guo Fu Li. "The Monitoring of Machine Tool Working State Based on Load Current Signal." Applied Mechanics and Materials 37-38 (November 2010): 1512–15. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.1512.

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According to the characteristic of machine tools such as complex driving chain and enclosed housing, this paper selects current signal which is easy to sample as the analytical signal. As the machines tools use different driving chain in different work state, this will affect motor current of machine tools; that is, the characteristics under different working conditions will be included in the current signal. This paper chose wavelet packet decomposition to analyze the current signal, then extracted wavelet packet coefficients of different frequency bands, by the change of wavelet packet coefficient to determine the machine's working condition. From the analysis of lathe current signal sampled in the experiment, it indicates the validity of wavelet packet coefficients as a feature quantity of the machine condition monitoring.

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42

Bauerdick, Christoph J. H., Mark Helfert, Lars Petruschke, Johannes Sossenheimer, and Eberhard Abele. "An automated procedure for workpiece quality monitoring based on machine drive-based signals in machine tools." Procedia CIRP 72 (2018): 357–62. http://dx.doi.org/10.1016/j.procir.2018.03.245.

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43

Han, Chun Guang, Yin Biao Guo, Hua Li, and Chen Jiang. "Distributed Monitoring System of Multi-Machine Tools Based on Wireless Sensor Network." Advanced Materials Research 97-101 (March 2010): 3527–30. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3527.

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In ultra precision machining processes, an effective monitoring system can maintain machine tools in the best condition and delay the occurrence of tool wear and improve the quality of workpiece. In this paper, a distributed system is developed for monitoring of multi-machine tool based on wireless sensor network. Firstly, the sensor in the machine tool is responsible for the information collection and data processing. Secondly, the wireless network is used to transmit the data or receive the commands from the computer of control center. Finally, the computer will monitor the overall system and generate the alarm signals or the commands when the change of environment information occurs. The experimental results have shown the effectiveness of the proposed distributed system.

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Sun, Wei-Heng, and Syh-Shiuh Yeh. "Using the Machine Vision Method to Develop an On-machine Insert Condition Monitoring System for Computer Numerical Control Turning Machine Tools." Materials 11, no. 10 (October 14, 2018): 1977. http://dx.doi.org/10.3390/ma11101977.

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Abstract:

This study uses the machine vision method to develop an on-machine turning tool insert condition monitoring system for tool condition monitoring in the cutting processes of computer numerical control (CNC) machines. The system can identify four external turning tool insert conditions, namely fracture, built-up edge (BUE), chipping, and flank wear. This study also designs a visual inspection system for the tip of an insert using the surrounding light source and fill-light, which can be mounted on the turning machine tool, to overcome the environmental effect on the captured insert image for subsequent image processing. During image capture, the intensity of the light source changes to ensure that the test insert has appropriate surface and tip features. This study implements outer profile construction, insert status region capture, insert wear region judgment, and calculation to monitor and classify insert conditions. The insert image is then trimmed according to the vertical flank, horizontal blade, and vertical blade lines. The image of the insert-wear region is captured to monitor flank or chipping wear using grayscale value histogram. The amount of wear is calculated using the wear region image as the evaluation index to judge normal wear or over-wear conditions. On-machine insert condition monitoring is tested to confirm that the proposed system can judge insert fracture, BUE, chipping, and wear. The results demonstrate that the standard deviation of the chipping and amount of wear accounts for 0.67% and 0.62%, of the average value, respectively, thus confirming the stability of system operation.

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Liu, Wei, Chuipin Kong, Qiang Niu, Jingguo Jiang, and Xionghui Zhou. "A method of NC machine tools intelligent monitoring system in smart factories." Robotics and Computer-Integrated Manufacturing 61 (February 2020): 101842. http://dx.doi.org/10.1016/j.rcim.2019.101842.

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Trompet, G. M., S. V. Butakov, N. K. Kazantseva, V. A. Aleksandrov, and A. S. Bubkin. "Dynamic Characteristics of an Active Vibrocontact Monitoring System for Multipurpose Machine Tools." Russian Engineering Research 38, no. 11 (November 2018): 876–78. http://dx.doi.org/10.3103/s1068798x18110175.

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Villalonga, Alberto, Gerardo Beruvides, Fernando Castaño, Rodolfo E. Haber, and Marcelino Novo. "Condition-based Monitoring Architecture for CNC Machine Tools based on Global Knowledge." IFAC-PapersOnLine 51, no. 11 (2018): 200–204. http://dx.doi.org/10.1016/j.ifacol.2018.08.259.

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Attia, M. H., and L. Kops. "Thermometric design considerations for temperature monitoring in machine tools and CMM structures." International Journal of Advanced Manufacturing Technology 8, no. 5 (September 1993): 311–19. http://dx.doi.org/10.1007/bf01783615.

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Shi, Xiao-Lei, Wei-Tang Sun, and Jia Song. "Design and implementation of real-time monitoring system for multiple machine tools." Procedia Computer Science 183 (2021): 274–80. http://dx.doi.org/10.1016/j.procs.2021.02.059.

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Schmucker, B., F. Trautwein, T. Semm, A. Lechler, M. F. Zaeh, and A. Verl. "Implementation of an Intelligent System Architecture for Process Monitoring of Machine Tools." Procedia CIRP 96 (2021): 342–46. http://dx.doi.org/10.1016/j.procir.2021.01.097.

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