EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 332 (2021) MATEC Web Conf., 332 (2021) 01014 References
Open Access
Issue
MATEC Web Conf.
Volume 332, 2021
19th International Conference Diagnostics of Machines and Vehicles “Hybrid Multimedia Mobile Stage”
Article Number 01014
Number of page(s) 11
DOI https://doi.org/10.1051/matecconf/202133201014
Published online 06 January 2021
  1. T. Kalaczynski, M. Lukasiewicz, J. Musial, R. Polasik, M. Szczutkowski, N. Dluhunovych, J. Wilczarska, T. Kasprowicz, Analysis of the diagnostic potential research thermovision in the technical state of combustion engine injectors assessment, 24th International Conference Engineering Mechanics, 357-360 (2018). [Google Scholar]
  2. J. Bhaskaran, M. Murugan, N. Balashanmugam, Monitoring of hard turning using acoustic emission signal, J Mech Sci Technol, 26, pp. 609-615 (2012). [Google Scholar]
  3. K. Dudzik, W. Labuda, The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process, Materials, 13, pp. 2926 (2020). [Google Scholar]
  4. A. Hamed, A.S. & Rahimi, In-Process Monitoring of Nickel-Based Super Alloy Grinding Using the Acoustic Emission Method, Russ J Nondestruct Test, 55, pp. 909-917 (2019). [Google Scholar]
  5. J.S. Kwak, J.B Song, Trouble diagnosis of the grinding process by using acoustic emission signals, International Journal of Machine Tools and Manufacture, Volume 41, Issue 6, pp. 899-913 (2001). [Google Scholar]
  6. S. Horiashchenko, K. Horiashchenko, J. Musial, Methodology of measuring spraying the droplet flow of polymers from nozzle, Mechanika, 26, pp. 82-86 (2020). [Google Scholar]
  7. S. Horiashchenko, J. Musial, K. Horiashchenko, R. Polasik, T. Kalaczynski, Mechanical Properties of Polymer Coatings Applied to Fabric, Polymers, 12 (11), pp. 2684 (2020). [Google Scholar]
  8. O. Synyuk, J. Musial, B. Zlotenko, T. Kulik, Development of Equipment for Injection Molding of Polymer Products Filled with Recycled Polymer Waste, Polymers, 12, pp. 2725 (2020). [Google Scholar]
  9. E. Dimla, D. Snr, M. Sortino, A New Approach to the Use of Vibration Signals in Tool-Wear Monitoring in High Speed Metal Turning (2002). [Google Scholar]
  10. H. Gao, M. Xu, Intelligent Tool Condition Monitoring System for Turning Operations, In: Wang J., Liao XF., Yi Z., (eds) Advances in Neural Networks – ISNN 2005, Lecture Notes in Computer Science, vol 3498, Springer, Berlin, Heidelberg (2005). [Google Scholar]
  11. S. Kosaraju, V.G. Anne, B.B. Popuri, Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modelling, Int J Adv Manuf Technol, 67, pp. 1947-1954 (2013). [Google Scholar]
  12. N. Li, Y. Chen, D. Kong, Force-based tool condition monitoring for turning process using v-support vector regression, Int J Adv Manuf Technol, 91, pp. 351-361 (2017). [Google Scholar]
  13. V. Marinescu, I. Constantin, C. Apostu, Adaptive dimensional control based on in-cycle geometry monitoring and programming for CNC turning center, Int J Adv Manuf Technol, 55, pp. 1079-1097 (2011). [Google Scholar]
  14. C.M. Nicolescu, M. Bejhem, On-Line Tool Condition Monitoring in Turning, Proceedings of the Thirty-First International Matador Conference, Palgrave, London (1995). [Google Scholar]
  15. B. Sick, On-line tool wear monitoring in turning using neural networks, Neural Comput & Applic, 7, pp. 356-366 (1998). [Google Scholar]
  16. S. Taibi, J. E. T. Penny, J.D. Maiden, M. Bennouna, Monitoring tool wear during the turning process, Condition Monitoring and Diagnostic Engineering Management, Springer, Dordrecht (1990). [Google Scholar]
  17. E. Waschkies, C. Sklarczyk, E. Schneider, Tool Wear Monitoring at Turning and Drilling, Nondestructive Characterization of Materials VI, Springer, Boston, MA (1994). [Google Scholar]
  18. C.F. Bisu, M. Zapciu, O. Cahuc, Envelope dynamic analysis: a new approach for milling process monitoring, Int J Adv Manuf Technol, 62, pp. 471-486 (2012). [Google Scholar]
  19. H. Fekrmandi, M. Unal, A. Baghalian, A non-contact method for part-based process performance monitoring in end milling operations, Int J Adv Manuf Technol, 83, pp. 13-20 (2016). [Google Scholar]
  20. S. Garnier, M. Ritou, B. Furet, J.Y. Hascoet, Comparision and Analisys of In-Process Tool Condition Monitoring Criterions in Milling, AMST’05 Advanced Manufacturing Systems and Technology, CISM International Centre for Mechanical Sciences (Courses and Lectures), vol 486, Springer, Vienna (2005). [Google Scholar]
  21. J. Lee, H.J. Choi, J. Nam, Development and analysis of an online tool condition monitoring and diagnosis system for a milling process and its real-time implementation, J Mech Sci Technol, 31, pp. 5695-5703 (2017). [Google Scholar]
  22. J. Musial, R. Polasik, T. Kalaczynski, M. Szczutkowski, M. Lukasiewicz, Milling efficiency aspects during machining of 7075 aluminium alloy with reference to the surface geometrical structure, In Proceedings of the 24th International Conference Engineering Mechanics, Svratka, Czech Republic, 14-17 May, pp. 569-572 (2018). [Google Scholar]
  23. M.K. Sinha, D. Setti, S. Ghosh, P. Venkateswara Rao, An investigation on surface burn during grinding of Inconel 718, Journal of Manufacturing Processes, Volume 21, pp. 124-133 (2016). [Google Scholar]
  24. R. Swiecik, R. Polasik, Energetic aspects of the AEDG grinding process of Ti6Al4V titanium alloy, In AIP Conference Proceedings, AIP Publishing LLC.: Melville, NY, USA, Volume 2017, p. 020031 (2018). [Google Scholar]
  25. R. Teti, T. Segreto, A. Caggiano, L. Nele, Smart Multi-Sensor Monitoring in Drilling of CFRP/CFRP, Composite Material Stacks for Aerospace Assembly Applications. Appl. Sci., 10, pp. 758 (2020). [Google Scholar]
  26. Y. Changfeng, W. Ting, X. Wei, H. Xinchun, R. Junxue, Experimental study on grinding force and grinding temperature of Aermet 100 steel in surface grinding, Journal of Materials Processing Technology, Volume 214, Issue 11, pp. 2191-2199 (2014). [Google Scholar]
  27. T. Segreto, A. Caggiano, S. Karam, R. Teti, Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation, Sensors, 17, pp. 2885 (2017). [Google Scholar]
  28. Y. Qin, D. Wang, Y. Yang, Integrated cutting force measurement system based on MEMS sensor for monitoring milling process, Microsyst Technol, 26, pp. 2095-2104 (2020). [Google Scholar]
  29. Y. Qin, Y. Zhao, Y. Li, A novel dynamometer for monitoring milling process, Int J Adv Manuf Technol, 92, pp. 2535-2543 (2017). [CrossRef] [Google Scholar]
  30. Y. Yang, B. Hao, X. Hao, A Novel Tool (Single-Flute) Condition Monitoring Method for End Milling Process Based on Intelligent Processing of Milling Force Data by Machine Learning Algorithms, Int. J. Precis. Eng. Manuf., 21, pp. 2159-2171 (2020). [Google Scholar]
  31. S. Mirifar, M. Kadivar, B. Azarhoushang, First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors, J. Manuf. Mater. Process, 4, pp. 35 (2020). [Google Scholar]
  32. P. Sutowski, K. Nadolny, W. Kaplonek, Monitoring of cylindrical grinding processes by use of a non-contact AE system, Int. J. Precis. Eng. Manuf., 13, pp. 1737-1743 (2012). [Google Scholar]
  33. E.M. Rubio, R. Teti, Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound, J Intell Manuf., 20, pp. 43 (2009). [Google Scholar]
  34. J. Musial, S. Horiashchenko, R. Polasik, J. Musial, T. Kalaczynski, M. Matuszewski, M. Srutek, Abrasion Wear Resistance of Polymer Constructional Materials for Rapid Prototyping and Tool-Making Industry, Polymers, 12, pp. 873 (2020). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.