Mechanical and Vehicle Engineering

Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering

September 29, 2023

Copyright (c) 2023 Krisztián Deák, József Menyhárt

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Deák, K., & Menyhárt, J. (2023). Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering. International Journal of Engineering and Management Sciences, 8(3), 72-89.

The optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.

  1. GraphIT: graphIT cégtörténet, in Hungarian, Downloaded: 2021.12.10. url:
  2. GraphIT: Tecnomatix digitális gyártási szoftvermegoldások, in Hungarian, Downloaded: 2021.12.10., url:
  3. SIM.TEC. - SIEMENS Tecnomatix PLANT SIMULATION - INDUSTRY 4.0: a tool to enhance the reality! Downloaded: 2021.12.10. url:
  4. GraphIT: Gyártási/logisztikai folyamat-szimuláció, optimalizáció, in Hungarian, Downloaded: 2021.12.10., url:
  5. GraphIT: Gyár, gyártósor és folyamat szimuláció és optimalizáció, Downloaded: 2021.12.10., Downloaded: 2021.12.10. url:
  6. TecnomatixCinema: plant_simulation.avi, Downloaded: 2021.12.11, url: (0:05)
  7. Graphitltd: Factory 51 Plant Simulation modell, Downloaded: 2021.12.11, url: (2:41)
  8. Siemens: Plant Simulation & Throughput Optimization, Downloaded: 2021.12.11, url:
  9. Karen Martin: Value Stream Mapping: How to Visualize Work and Align Leadership for Organizational Transformation, McGraw-Hill, ISBN: 0071828915, 2013.
  10. GraphIT: Értékáram elemzés (Value stream map, VSM), in Hungarian, Downloaded: 2021.12.11, url:
  11. GraphIT: SmartTalk könyvtár, in Hungarian, Downloaded: 2021.12.11., url:
  12. A. Rivero, L.N. López de Lacalle, M. L. Penalv (2008) Tool wear detection in dry high-speed milling based upon the analysis of machine internal signals, Mechatronics, Vol 18, pp 627–633.
  13. Clarence W. de Silva (2005) Vibration and shock handbook, ISBN 0-8493-1580
  14. D.E. Dimla Sr. a, P.M. Lister (2000) On-line metal cutting tool condition monitoring.II: tool-state classification using multi-layer perceptron neural networks, International Journal of Machine Tools & Manufacture, Vol 40, pp 769–781.
  15. D.X. Fang, Y. Yao and G. Amdt (1991) Monitoring groove wear development in cutting tools via stochastic modelling of three dimensional vibration, Wear, 151, 143-156.
  16. J.E. Weller, M.H. Schrier and B. Weichbrodt (1969) What sound can be expected from a worn tool?, Trans. ASME, J. Eng. Indust., 13, 525-534.
  17. Kopac, J. (1998) Influence of cutting material and coating on tool quality and tool life J. Mater. Process. Technol., 78, 95-103
  18. M. Balazinski, E. Czogala, K. Jemielniak, J. Leski (2002) Tool condition monitoring using artificial intelligence methods, Engineering Applications of Artificial Intelligence, Vol 15 , pp 73–80.
  19. M.S. Pandit and S. Kashou (1982) A data dependent system strategy of on-line tool wear sensing, Trans. ASME, J. Eng. Indust., 104, 217-223.
  20. N. Akihiko and S. Fujita (1989) Development of a cutting tool failure detector, BUZZ. Jpn. Sot. Prec. Eng., 23., 134-139.
  21. P. Martin, B. Mute1 and J.D. Drapier (1974) Influence of lathe tool wear on the vibration sustained in cutting, Proc. 15th int. Machine Tool Design and Research Conf , pp. 251-274.
  22. P. Wilkinson, R. L. Reuben (1999) Tool wear perdition from acoustic emission and surface characteristic vi an artificial neural network, Mechanical Systems and Signal Processing, 13(6), pp 955-966.
  23. R. E. Haber, A. Alique (2003) Intelligent process supervision for predicting tool wear in machining processes, Mechatronics, Vol 13, pp 825–849.
  24. S. Orhan, A. Osman Er, N. Camus-cu, E. Aslan (2007) Tool wear evaluation by vibration analysis dur ing end milling of AISI D3 cold work tool steel with 35 HRC hardness, NDT&E International, Vol 40, pp 121–126.
  25. T. J. Ko, D. W. Cho (1993) Estimation of tool wear length in finish milling using a fuzzy inference algorithm, Wear, Vol 169, pp 97-106.
  26. T. Ozel, A. Nadgir (2002) Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools, International Journal of Machine Tools & Manufacture, Vol 42, pp 287–297.
  27. U. Zuperl, F. Cus (2004) Tool cutting force modeling in ball-end milling using multilevel perceptron, Journal of Materials Processing Technology, Vol 153–154 pp 268–275.
  28. Y.C. Jiang and J.H. Xu (1987) In-process monitoring of tool wear stage by the frequency band energy method, Ann. CIRP, 36, 45-48.
  29. Microsystems . precision medical moduls: Success factors micro milling hardened, Downloaded: 2021.11.08., url:
  30. Devopedia: Artificial Neural Network, Downloaded: 2021.11.08. url:
  31. Researchgate: Rohy Teguh: Optimization Topology of Energy Constrained Wireless Sensor Networks, Downloaded: 2021.11.08. url:
  32. Researchgate: Lluvia M. Ochoa-Estopier, Megan Jobson, Robin Smith, Lu Chen, Clemente Rodriguez, Optimization of Heat-Integrated Crude Oil Distillation Systems. Part II: Heat Exchanger Network Retrofit Model, Downloaded: 2021.11.09., url:
  33. Failure of Cutting Tools and Tool Wear, presentation, Downloaded: 2021.11.09., url:
  34. Balogh István: Anyagáramlás optimalizálása az FAG Magyarország Ipari Kft-nél, thesis, in Hungarian, University of Debrecen, Faculty of Engineering, 2020.
  35. T. Mohanraj *, Jayanthi Yerchuru , H. Krishnan , R.S. Nithin Aravind , R. Yameni (2021) Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms, Measurement 173.
  36. T. Mohanraj, S. Shankar, R. Rajasekar, N.R. Sakthivel, A. Pramanik (2020) Tool condition monitoring techniques in milling process -A review, Journal of Materials Research and Technology, Vol. 9(1):1032–1042
  37. S. Shankar, T. Mohanraj & R. Rajasekar (2018) Prediction of cutting tool wear during milling process using artificial intelligence techniques, International Journal of Computer Integrated Manufacturing, Vol. 32:2, 174-182, DOI: 10.1080/0951192X.2018.1550681
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