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Journal articles on the topic 'Tool Condition Monitoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

KUMAKURA, R., Y. KAKINUMA, T. ARAI, E. UCHISHIBA, M. MURAKAMI, T. SAGARA, and T. AOYAMA. "D004 Sensorless tool condition monitoring in buffing processes." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2013.7 (2013): 463–68. http://dx.doi.org/10.1299/jsmelem.2013.7.463.

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2

Horváth, L., and B. Szabó. "Tool Condition Monitoring System." IFAC Proceedings Volumes 19, no. 13 (November 1986): 291–95. http://dx.doi.org/10.1016/s1474-6670(17)59556-7.

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3

GOLOVIN, V. I., and S. Yu RADCHENKO. "TOOL CONDITION MONITORING SYSTEM IN SERIAL PRODUCTION CONDITIONS." Fundamental and Applied Problems of Engineering and Technology 4, no. 2 (2020): 161–68. http://dx.doi.org/10.33979/2073-7408-2020-342-4-2-161-168.

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One of the most important tasks of serial and mass production is to maintain the continuity of the technological process in order to reduce equipment downtime and, as a result, the cost of production. One of the systems is the tool condition monitoring system. However, the solutions used today are complex software and hardware systems that are not available for most medium and small productions. The article proposes a system based on a comparative analysis of the applied tool with reference instances. The results of the analysis are sent to the decision-making system, which determines the feasibility of further use of the cutting tool for subsequent machining. An example of an experimental study of milling processing is given. The results obtained show the possibility and rationality of using this model to predict the state of the instrument.

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4

Axinte, Dragoş A., and Nabil Gindy. "Tool condition monitoring in broaching." Wear 254, no. 3-4 (February 2003): 370–82. http://dx.doi.org/10.1016/s0043-1648(03)00003-6.

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5

Jemielniak, Krzysztof. "Tool and process condition monitoring." Mechanik 90, no. 7 (July 10, 2017): 504–10. http://dx.doi.org/10.17814/mechanik.2017.7.64.

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Automatic tool condition monitoring is based on the measurements of physical phenomena which are correlated with this condition. There are numerous signal features (SFs) that can be extracted from the signal. As it is really not possible to predict which signal features will be useful in a particular case they should be automatically selected and combined into one tool condition estimation. This can be achieved by various artificial intelligence methods.

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6

Jemielniak, K. "Commercial Tool Condition Monitoring Systems." International Journal of Advanced Manufacturing Technology 15, no. 10 (September 1999): 711–21. http://dx.doi.org/10.1007/s001700050123.

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7

Visariya, Ramesh, Ronak Ruparel, and Rahul Yadav. "Review of Tool Condition Monitoring Methods." International Journal of Computer Applications 179, no. 37 (April 18, 2018): 29–32. http://dx.doi.org/10.5120/ijca2018916853.

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8

Ambhore, Nitin, Dinesh Kamble, Satish Chinchanikar, and Vishal Wayal. "Tool Condition Monitoring System: A Review." Materials Today: Proceedings 2, no. 4-5 (2015): 3419–28. http://dx.doi.org/10.1016/j.matpr.2015.07.317.

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9

Du, R. "Signal understanding and tool condition monitoring." Engineering Applications of Artificial Intelligence 12, no. 5 (October 1999): 585–97. http://dx.doi.org/10.1016/s0952-1976(99)00026-3.

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10

Holroyd, Trevor J. "Acoustic Emission — The Condition Monitoring Tool." Measurement and Control 34, no. 4 (May 2001): 115–16. http://dx.doi.org/10.1177/002029400103400409.

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11

JEMIELNIAK, Krzysztof. "CONTEMPORARY CHALLENGES IN TOOL CONDITION MONITORING." Journal of Machine Engineering 19, no. 1 (February 20, 2019): 48–61. http://dx.doi.org/10.5604/01.3001.0013.0448.

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Implementation of robust, reliable tool condition monitoring (TCM) systems in one of the preconditions of introducing of Industry 4.0. While there are a huge number of publications on the subject, most of them concern new, sophisticated methods of signal feature extraction and AI based methods of signal feature integration into tool condition information. Some aspects of TCM algorithms, namely signal segmentation, selection of useful signal features, laboratory measured tool wear as reference value of tool condition – are nowadays main obstacles in the broad application of TCM systems in the industry. These aspects are discussed in the paper, and some solutions of the problems are proposed.

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12

Inţă, Marinela, Achim Muntean, and Sorin-Mihai Croitoru. "Researches regarding cutting tool condition monitoring." MATEC Web of Conferences 121 (2017): 02002. http://dx.doi.org/10.1051/matecconf/201712102002.

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13

Ilonen, J., J. K. Kamarainen, T. Lindh, J. Ahola, H. Kalviainen, and J. Partanen. "Diagnosis Tool for Motor Condition Monitoring." IEEE Transactions on Industry Applications 41, no. 4 (July 2005): 963–71. http://dx.doi.org/10.1109/tia.2005.851001.

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14

Vrabeľ, Marek, Miroslav Paľo, Mária Semanová, Ildikó Maňková, and Jozef Trebuňa. "Tool Condition Monitoring when Hard Machining." Acta Mechanica Slovaca 24, no. 2 (June 22, 2020): 20–28. http://dx.doi.org/10.21496/ams.2020.019.

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15

Wong, Y. S., A. Y. C. Nee, X. Q. Li, and C. Reisdorf. "Tool condition monitoring using laser scatter pattern." Journal of Materials Processing Technology 63, no. 1-3 (January 1997): 205–10. http://dx.doi.org/10.1016/s0924-0136(96)02625-8.

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16

Bombiński, Sebastian, Krzysztof Błażejak, Mirosław Nejman, and Krzysztof Jemielniak. "Sensor Signal Segmentation for Tool Condition Monitoring." Procedia CIRP 46 (2016): 155–60. http://dx.doi.org/10.1016/j.procir.2016.03.203.

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17

Dimla, D. E., and P. M. Lister. "On-line metal cutting tool condition monitoring." International Journal of Machine Tools and Manufacture 40, no. 5 (April 2000): 739–68. http://dx.doi.org/10.1016/s0890-6955(99)00084-x.

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18

Dimla, D. E., and P. M. Lister. "On-line metal cutting tool condition monitoring." International Journal of Machine Tools and Manufacture 40, no. 5 (April 2000): 769–81. http://dx.doi.org/10.1016/s0890-6955(99)00085-1.

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19

Balazinski, Marek, Ernest Czogala, Krzysztof Jemielniak, and Jacek Leski. "Tool condition monitoring using artificial intelligence methods." Engineering Applications of Artificial Intelligence 15, no. 1 (February 2002): 73–80. http://dx.doi.org/10.1016/s0952-1976(02)00004-0.

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20

Coady, James, Daniel Toal, Thomas Newe, and Gerard Dooly. "Remote acoustic analysis for tool condition monitoring." Procedia Manufacturing 38 (2019): 840–47. http://dx.doi.org/10.1016/j.promfg.2020.01.165.

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21

Li, Huaizhong, Xiaoqi Chen, Hao Zeng, and Xiaoping Li. "Embedded tool condition monitoring for intelligent machining." International Journal of Computer Applications in Technology 28, no. 1 (2007): 74. http://dx.doi.org/10.1504/ijcat.2007.012334.

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22

Gittler, Thomas, Fabian Stoop, David Kryscio, Lukas Weiss, and Konrad Wegener. "Condition monitoring system for machine tool auxiliaries." Procedia CIRP 88 (2020): 358–63. http://dx.doi.org/10.1016/j.procir.2020.05.062.

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23

Engeler, Marc, Andreas Elmiger, Andreas Kunz, David Zogg, and Konrad Wegener. "Online Condition Monitoring Tool for Automated Machinery." Procedia CIRP 58 (2017): 323–28. http://dx.doi.org/10.1016/j.procir.2017.04.003.

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24

Hoh, S. M., P. Thorpe, K. Johnston, and K. F. Martin. "Sensor Based Machine Tool Condition Monitoring System." IFAC Proceedings Volumes 21, no. 15 (September 1988): 103–10. http://dx.doi.org/10.1016/s1474-6670(17)54684-4.

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25

Painuli, Sanidhya, M. Elangovan, and V. Sugumaran. "Tool condition monitoring using K-star algorithm." Expert Systems with Applications 41, no. 6 (May 2014): 2638–43. http://dx.doi.org/10.1016/j.eswa.2013.11.005.

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26

Liu, Tien-I., and Bob Jolley. "Tool condition monitoring (TCM) using neural networks." International Journal of Advanced Manufacturing Technology 78, no. 9-12 (January 21, 2015): 1999–2007. http://dx.doi.org/10.1007/s00170-014-6738-y.

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27

Jain, Amit Kumar, and Bhupesh Kumar Lad. "A novel integrated tool condition monitoring system." Journal of Intelligent Manufacturing 30, no. 3 (June 2, 2017): 1423–36. http://dx.doi.org/10.1007/s10845-017-1334-2.

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28

Kassim, A. A., M. A. Mannan, and Zhu Mian. "Texture analysis methods for tool condition monitoring." Image and Vision Computing 25, no. 7 (July 2007): 1080–90. http://dx.doi.org/10.1016/j.imavis.2006.05.024.

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29

Lutz, Benjamin, Philip Howell, Daniel Regulin, Bastian Engelmann, and Jörg Franke. "Towards Material-Batch-Aware Tool Condition Monitoring." Journal of Manufacturing and Materials Processing 5, no. 4 (September 27, 2021): 103. http://dx.doi.org/10.3390/jmmp5040103.

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In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.

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30

Zhang, Guohai, and Huibin Sun. "Enabling a cutting tool iPSS based on tool condition monitoring." International Journal of Advanced Manufacturing Technology 94, no. 9-12 (August 6, 2017): 3265–74. http://dx.doi.org/10.1007/s00170-017-0852-6.

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31

Laghmouchi, Abdelhakim, Eckhard Hohwieler, Claudio Geisert, and Eckart Uhlmann. "Intelligent Configuration of Condition Monitoring Algorithms." Applied Mechanics and Materials 794 (October 2015): 355–62. http://dx.doi.org/10.4028/www.scientific.net/amm.794.355.

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The aim of this paper is to present the design of a condition monitoring tool, its use for the intelligent configuration of pattern recognition algorithms, for fault detection, and for diagnosis applications. The modular design and functionality of the tool will be introduced. The tool, developed and implemented by Fraunhofer IPK, can be used, in particular, to support the development process of algorithms for condition monitoring of wear-susceptible components in production systems. An example of the industrial application of the tool will be presented. This will include the implementation of configured algorithms using the tool on an embedded system using Raspberry Pi 2 and MEMS sensor. Finally, the evaluation of these algorithms on an axis test rig at different operating parameters will be presented.

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32

El Ouafi, Abderrazak, Michel Guillot, and Noureddine Barka. "Cutting Tool Condition Monitoring in Machining Processes - A Comprehensive Approach Using ANN Based Multisensor Fusion Strategy." Applied Mechanics and Materials 232 (November 2012): 966–72. http://dx.doi.org/10.4028/www.scientific.net/amm.232.966.

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On-line cutting tool condition monitoring becomes one of the most critical requirements in machining processes for improving the efficiency and the autonomy of CNC machine tools. The processes can be significantly improved by using an intelligent integration of sensor information to detect and identify accurately the tool condition under various cutting parameters. This paper presents a structured and comprehensive approach for tool condition monitoring in machining processes using ANN based multisensor fusion strategy. Various sensing techniques are combined to select suitable monitoring indices and several models are proposed to establish the relationship between tool condition and the selected monitoring indices. The proposed approach is built progressively by examining monitoring indices from various aspects and making monitoring decision step by step. The results indicate a significant improvement and a good reliability in identifying various tool conditions regardless of the variation in cutting parameters.

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33

Roberto Ingraci Neto, Rubens, Renan Luis Fragelli, Arthur Alves Fiocchi, and Luiz Eduardo de Angelo Sanchez. "Tool Condition Monitoring Using the Electromotive Force from the Chip-Tool Thermocouple." Applied Mechanics and Materials 798 (October 2015): 271–75. http://dx.doi.org/10.4028/www.scientific.net/amm.798.271.

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Tool condition monitoring systems are extensively study. However, the machining processes are non-stationary and comprise many details that interfere in its monitoring. Aiming to develop a simple, low cost and efficient tool condition monitoring system, this study analyzed the electromotive force (EMF) from a chip-tool thermocouple in turning tests with AISI 1045. Since EMF comprises time and frequency variations related to machining conditions a Wavelet Packet Transform extracted the signals features from EMF. These signals features fed inputs of a neural network that aimed to evaluate the cutting tool maximum flank wear. The maximum error of the neural network was 1.88% for tested signals. Moreover, EMF showed changes that allow the detection of cutting tool breakage. Therefore, the chip-tool thermocouple may be a promising method for tool condition monitoring. This is the first report of electromotive force analysis in time-frequency domain aiming to quantify the wear of the cutting tool and evaluate its condition.

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34

Denkena, Berend, Benjamin Bergmann, and Tobias H. Stiehl. "Transfer of Process References between Machine Tools for Online Tool Condition Monitoring." Machines 9, no. 11 (November 10, 2021): 282. http://dx.doi.org/10.3390/machines9110282.

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Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face-turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density-based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.

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35

Koike, Ryo, Ryo Kumakura, Takashi Arai, Eishiro Uchishiba, Makoto Murakami, Takahisa Sagara, and Yasuhiro Kakinuma. "Sensorless Tool Stiffness Monitoring in Buffing." International Journal of Automation Technology 8, no. 6 (November 5, 2014): 827–36. http://dx.doi.org/10.20965/ijat.2014.p0827.

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In current practice, the buffing process required to finish the surface of mechanical parts is performed manually by a technical expert as it requires a delicate adjustment of the buffing force. The automation of this process is desirable in an effort to shorten the process time and reduce labor cost. To automate the buffing process, a beneficial process monitoring technique that supervises the buffing tool conditions in real time must be developed. From a practical perspective, an observer technique that does not require additional sensors would be most suitable for monitoring the tool operating condition. The authors propose a technique that estimates the buffing tool stiffness based on a disturbance observer. The validity of the proposed method as a buffing tool condition monitoring technique is verified through numerical simulations and experiments.

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36

Wuerschinger, H., D. Gross, M. Muehlbauer, M. Stadler, and N. Hanenkamp. "DEMONSTRATION OF A NEW APPROACH FOR MEASURING TOOLS WITH THE IMPINGEMENT SOUND OF AN AIR JET USING MACHINE LEARNING." MM Science Journal 2021, no. 5 (November 3, 2021): 4984–91. http://dx.doi.org/10.17973/mmsj.2021_11_2021139.

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Monitoring the tool condition of machining processes is important but challenging. Several automated tool condition monitoring solutions are available, but often not used due to existing restrictions or disadvantages. A new approach can be the detection and measurement of tool conditions analyzing the sound of an air jet impingement on tools. Due to the availability of compressed air as a working and cleaning medium for many processes, this approach can be used for various condition monitoring and measuring tasks. In this paper the procedure and its functionality are first presented on simple shapes and then tested on the tool wear of inserts.

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37

Yao, Ying Xue, Y. Lu, Zhe Jun Yuan, and J. Y. Hu. "A Hybrid Model for Tool Condition Monitoring and Optimal Tool Management." Materials Science Forum 471-472 (December 2004): 865–70. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.865.

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This paper introduces a new hybrid model for tool condition monitoring (TCM) and optimal tool management (OTM) in end milling operation. The model includes a wavelet fuzzy neural network with acoustic emission (AE) and a model of fuzzy classification of tool wear state with the detected cutting parameters supported by cutting database. The results estimated by cutting conditions and detected signals are fused by artificial neural network (ANN) so as to facilitate effective tool replacement at a proper state or time. The validity and reliability of the method are verified by experimental results.

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38

Ao, Yin Hui. "Monitoring of Drilling Tool Condition through Spindle Current." Advanced Materials Research 139-141 (October 2010): 2595–98. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2595.

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Drill wear or breakage often damages the work piece and/or machine tool. Spindle motor current reflects the cutting process and the signal can be easily and inexpensively obtained. This paper presents a strategy for on-line drilling tool wear and breakage monitoring. It employs Wavelet Transform (WT) of the spindle current signature to perform monitoring. A moving window technique is used to extract the cutting portion of data from the entire data sequence. A low pass de-nosing filter is employed to remove noise from the current signal. Features were extracted using WT node energy and selected based on their ability to detect tool wear and chipping. The Progression of tool wear based on feature of WT detail level 4 is analyzed and pointed out status of worn or chipped tool. Experimental results validate performance of the proposed algorithm.

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39

Čuš, Franci, and Uroš Župerl. "Real-Time Cutting Tool Condition Monitoring in Milling." Strojniški vestnik – Journal of Mechanical Engineering 57, no. 2 (February 15, 2011): 142–50. http://dx.doi.org/10.5545/sv-jme.2010.079.

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40

Heyns, P. S. "Tool condition monitoring using vibration measurements a review." Insight - Non-Destructive Testing and Condition Monitoring 49, no. 8 (August 1, 2007): 447–50. http://dx.doi.org/10.1784/insi.2007.49.8.447.

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41

Zhu, Aibin, Dayong He, Jianwei Zhao, and Hongling Wu. "Online tool wear condition monitoring using binocular vision." Insight - Non-Destructive Testing and Condition Monitoring 59, no. 4 (April 1, 2017): 203–10. http://dx.doi.org/10.1784/insi.2017.59.4.203.

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42

Martin, K. F., S. M. Hoh, and J. H. Williams. "Condition Monitoring Machine Tool Drives via Health Indices." IFAC Proceedings Volumes 24, no. 6 (September 1991): 571–76. http://dx.doi.org/10.1016/s1474-6670(17)51202-1.

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43

Szecsi, Tamas. "Automatic cutting-tool condition monitoring on CNC lathes." Journal of Materials Processing Technology 77, no. 1-3 (May 1998): 64–69. http://dx.doi.org/10.1016/s0924-0136(97)00395-6.

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44

Zhu, Kunpeng, Guochao Li, and Yu Zhang. "Big Data Oriented Smart Tool Condition Monitoring System." IEEE Transactions on Industrial Informatics 16, no. 6 (June 2020): 4007–16. http://dx.doi.org/10.1109/tii.2019.2957107.

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45

Zhu, Kunpeng, and Xin Lin. "Tool Condition Monitoring With Multiscale Discriminant Sparse Decomposition." IEEE Transactions on Industrial Informatics 15, no. 5 (May 2019): 2819–27. http://dx.doi.org/10.1109/tii.2018.2867451.

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46

Dasarathy, Belur V. "Information fusion as a tool in condition monitoring." Information Fusion 4, no. 2 (June 2003): 71–73. http://dx.doi.org/10.1016/s1566-2535(03)00019-8.

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47

Amer, W., R. Grosvenor, and P. Prickett. "Machine tool condition monitoring using sweeping filter techniques." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 1 (February 2007): 103–17. http://dx.doi.org/10.1243/09596518jsce133.

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48

Swain, Samarjit, Isham Panigrahi, Ashok Kumar Sahoo, and Amlana Panda. "Adaptive tool condition monitoring system: A brief review." Materials Today: Proceedings 23 (2020): 474–78. http://dx.doi.org/10.1016/j.matpr.2019.05.386.

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49

Dornfeld, David A., and M. F. DeVries. "Neural Network Sensor Fusion for Tool Condition Monitoring." CIRP Annals 39, no. 1 (1990): 101–5. http://dx.doi.org/10.1016/s0007-8506(07)61012-9.

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50

Yesilyurt, I., and H. Ozturk. "Tool condition monitoring in milling using vibration analysis." International Journal of Production Research 45, no. 4 (February 15, 2007): 1013–28. http://dx.doi.org/10.1080/00207540600677781.

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