Mechanics & Industry
Volume 25, 2024
Advanced Approaches in Manufacturing Engineering and Technologies Design
Article Number 17
Number of page(s) 10
Published online 23 May 2024
  1. E. Assidjo, B. Yao, K. Kisselmina, D. Amané, Modeling of an industrial drying process by artificial neural networks, Braz. J. Chem. Eng. 25, 515–522 (2008) [Google Scholar]
  2. E. Tafazzoli, M. Saif, Application of combined support vector machines in process fault diagnosis, Proc. Am. Control Conf., 3429–3433, Publisher: IEEE, St. Louis, MO, USA (2009) [Google Scholar]
  3. M. Deja, M. Siemiatkowski, Machining process sequencing and machine assignment in generative feature-based CAPP for mill-turn parts, J. Manuf. Syst. 48, 49–62 (2018) [Google Scholar]
  4. N. Rehman, Data mining techniques methods algorithms and tools, Int. J. Comput. Sci. Mob. Comput. 6, 227–231 (2017) [Google Scholar]
  5. P. Denno, C. Dickerson, J.A. Harding, Dynamic production system identification for smart manufacturing systems, J. Manuf. Syst. 48, 1–11 (2018) [Google Scholar]
  6. R. Corne, C. Nath, M. Mansori, T. Kurfess, Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling, J. Manuf. Syst. 43, 287–295 (2017) [Google Scholar]
  7. S.B. Kotsiantis, Supervised machine learning, a review of classification techniques, Informatica 31, 249–268 (2007) [Google Scholar]
  8. W. Su, M. Bo, Ant colony optimization for manufacturing resource scheduling problem, IFIP Int. Federat. Inf. Process. 207, 863–868 (2006) [Google Scholar]
  9. Y. Song, J. Huang, D. Zhou, H. Zha, C.L. Giles, Informative K-nearest neighbor pattern classification, knowledge discovery in databases: PKDD 2007, in Lecture Notes in Computer Science, vol. 4702 (2007). pp. 248–264 [Google Scholar]
  10. Z.M. Bi, L. Wang, Optimization of machining processes from the perspective of energy consumption: a case study, J. Manuf. Syst. 31, 420–428 (2012) [Google Scholar]
  11. M. Rogalewicz, M. Piłacińska, A. Kujawińska, Selection of data mining method for multidimensional evaluation of the manufacturing process state, Manag. Prod. Eng. Rev. 3, 27–35 (2012) [Google Scholar]
  12. R. Sika, Z. Ignaszak, Data acquisition in modeling using neural networks and decision trees, Arch. Foundry Eng. 11, 113–121 (2011) [Google Scholar]
  13. Feature selection, last accessed 2022 /05/10 [Google Scholar]
  14. G.R. Frumusanu, C. Afteni, A. Epureanu, Data-driven causal modelling of the manufacturing system, Trans. Famena. 45, 43–62 (2021) [Google Scholar]
  15. C. Afteni, G.R. Frumusanu, A. Epureanu, Method for holistic optimization of the manufacturing process, int. J. Model. Optim. 9, 265–270 (2019) [Google Scholar]
  16. C. Afteni, G.R. Frumusanu, A. Epureanu, Instance-based comparative assessment with application in manu-facturing, IOP Conf. Ser.: Mater. Sci. Eng. 400, 1–8 (2018) [Google Scholar]
  17. Decision support system,, last accessed 2022 /05/10 [Google Scholar]
  18. Instance-based learning,, last accessed 2022 /05/10 [Google Scholar]
  19. C. Afteni, Holistic optimization of manufacturing process, PhD Thesis, ’Dunarea de Jos’ University of Galati, Series I 4: Industrial Engineering (2020) [Google Scholar]
  20. C. Afteni, M. Afteni, G.R. Frumusanu, Study on the application of the holistic optimization method of the manufacturing process in the case of a reduced instances database, MATEC Web Conf. 368, 1–10 (2022) [Google Scholar]

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