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Original scientific paper

Surface quality prediction by artificial-neural-networks

Goran Šimunović   ORCID icon orcid.org/0000-0002-7159-2627 ; Sveučilište J. J. Strossmayera u Osijeku, Strojarski fakultet u Slavonskom Brodu, Trg Ivane Brlić-Mažuranić 2, 35000 Slavonski Brod, Croatia
Tomislav Šarić   ORCID icon orcid.org/0000-0002-6339-7936 ; Sveučilište J. J. Strossmayera u Osijeku, Strojarski fakultet u Slavonskom Brodu, Trg Ivane Brlić-Mažuranić 2, 35000 Slavonski Brod, Croatia
Roberto Lujić   ORCID icon orcid.org/0000-0001-5123-3064 ; Sveučilište J. J. Strossmayera u Osijeku, Strojarski fakultet u Slavonskom Brodu, Trg Ivane Brlić-Mažuranić 2, 35000 Slavonski Brod, Croatia

Fulltext: croatian, pdf (237 KB) pages 43-47 downloads: 1.301* cite
APA 6th Edition
Šimunović, G., Šarić, T. & Lujić, R. (2009). Primjena neuronskih mreža u procjenjivanju kvalitete obrađivane površine. Tehnički vjesnik, 16 (2), 43-47. Retrieved from https://hrcak.srce.hr/38666
MLA 8th Edition
Šimunović, Goran, et al. "Primjena neuronskih mreža u procjenjivanju kvalitete obrađivane površine." Tehnički vjesnik, vol. 16, no. 2, 2009, pp. 43-47. https://hrcak.srce.hr/38666. Accessed 18 Nov. 2019.
Chicago 17th Edition
Šimunović, Goran, Tomislav Šarić and Roberto Lujić. "Primjena neuronskih mreža u procjenjivanju kvalitete obrađivane površine." Tehnički vjesnik 16, no. 2 (2009): 43-47. https://hrcak.srce.hr/38666
Harvard
Šimunović, G., Šarić, T., and Lujić, R. (2009). 'Primjena neuronskih mreža u procjenjivanju kvalitete obrađivane površine', Tehnički vjesnik, 16(2), pp. 43-47. Available at: https://hrcak.srce.hr/38666 (Accessed 18 November 2019)
Vancouver
Šimunović G, Šarić T, Lujić R. Primjena neuronskih mreža u procjenjivanju kvalitete obrađivane površine. Tehnički vjesnik [Internet]. 2009 [cited 2019 November 18];16(2):43-47. Available from: https://hrcak.srce.hr/38666
IEEE
G. Šimunović, T. Šarić and R. Lujić, "Primjena neuronskih mreža u procjenjivanju kvalitete obrađivane površine", Tehnički vjesnik, vol.16, no. 2, pp. 43-47, 2009. [Online]. Available: https://hrcak.srce.hr/38666. [Accessed: 18 November 2019]

Abstracts
Surface roughness is often taken as an important indicator of the quality of machined parts. Achieving the desired surface quality is of great importance for the product function. In this paper, influence of material, type of tool, cutting depth, feed rate and cutting speed on surface roughness is observed. Collected results of experimental research are utilized for surface roughness prediction using neural networks. Various structures of a back-propagation neural network have been analyzed and the network with the minimum RMS error has been selected. Evaluation of surface roughness obtained by neural networks model can help to less experienced technologists and therefore production preparation technological time can be shorter.

Keywords
surface quality; artificial intelligence; neural networks

Hrčak ID: 38666

URI
https://hrcak.srce.hr/38666

[croatian]

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