PREDIKTIVNO MODELOVANJE HRAPAVOSTI POVRŠINE PRI OBRADI GLODANJEM LEGURE TITANIJUMA Ti-6Al-4V UZ UPOTREBU MAŠINSKOG UČENJA

  • Mile Đurić
  • Borislav Savković Fakultet tehničkih nauka, Departman za proizvodno mašinstvo
Ključne reči: Taguči, hrapavost, Minitab, MATLAB, modelovanje, neuronske mreže, glodanje

Apstrakt

Analiziranje uticaja ulaznih faktora na izlaznu veličinu (hrapavost), modelovanje optimalne kombinacije faktora u cilju postizanja bolje hrapavosti obrađene površine.

Reference

[1] Ugochi, D., Yimin, Z., Kranthi, K.D., (2018). Unsupervised Learning Based On Artificial Neural Network: A Review, International Conference on Cyborg and Bionic Systems, pp. 323-327.
[2] Tlhabadira, I.,Daniyan, I.A.,Machaka, R.,Machio, C.,Masu, L., VanStaden, L.R., (2019). Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method, The International Journal of Advanced Manufacturing Technology, pp.3707-3718.
[3] Kovač, P., (2011). Metode planiranja i obrade eksperimenta, Fakultet tehničkih nauka.
[4] Canakci, A., Erdemir, F., Varol, T., Patir, A., (2013). Determining the effect of process parameters on particle size in mechanical milling using the Taguchi method: Measurement and analysis, Elsevir; Measurment 46, pp. 3532-3540.
[5] Mao, H., Jiao, L., Gao, S., Yi, J., Peng, Z., Liu, Z., Yan, P., Wang, X., (2016). Surface quality evaluation in meso-scale end-milling operation based on fractal theory and the Taguchi method, International Journal Advanced Manufacturing Technology 91, pp. 657-665.
Objavljeno
2024-01-31
Sekcija
Mašinsko inženjerstvo