Issue |
Mechanics & Industry
Volume 25, 2024
Advanced Approaches in Manufacturing Engineering and Technologies Design
|
|
---|---|---|
Article Number | 17 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/meca/2024012 | |
Published online | 23 May 2024 |
- 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) [CrossRef] [Google Scholar]
- 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]
- 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) [CrossRef] [Google Scholar]
- N. Rehman, Data mining techniques methods algorithms and tools, Int. J. Comput. Sci. Mob. Comput. 6, 227–231 (2017) [Google Scholar]
- P. Denno, C. Dickerson, J.A. Harding, Dynamic production system identification for smart manufacturing systems, J. Manuf. Syst. 48, 1–11 (2018) [Google Scholar]
- 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) [CrossRef] [Google Scholar]
- S.B. Kotsiantis, Supervised machine learning, a review of classification techniques, Informatica 31, 249–268 (2007) [Google Scholar]
- W. Su, M. Bo, Ant colony optimization for manufacturing resource scheduling problem, IFIP Int. Federat. Inf. Process. 207, 863–868 (2006) [Google Scholar]
- 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 [CrossRef] [Google Scholar]
- 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) [CrossRef] [Google Scholar]
- 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]
- R. Sika, Z. Ignaszak, Data acquisition in modeling using neural networks and decision trees, Arch. Foundry Eng. 11, 113–121 (2011) [Google Scholar]
- Feature selection, https://en.wikipedia.org/wiki/Main_Page last accessed 2022 /05/10 [Google Scholar]
- G.R. Frumusanu, C. Afteni, A. Epureanu, Data-driven causal modelling of the manufacturing system, Trans. Famena. 45, 43–62 (2021) [CrossRef] [Google Scholar]
- 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]
- 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]
- Decision support system, https://en.wikipedia.org/wiki/Main_Page, last accessed 2022 /05/10 [Google Scholar]
- Instance-based learning, https://en.wikipedia.org/wiki/Main_Page, last accessed 2022 /05/10 [Google Scholar]
- C. Afteni, Holistic optimization of manufacturing process, PhD Thesis, ’Dunarea de Jos’ University of Galati, Series I 4: Industrial Engineering (2020) [Google Scholar]
- 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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