Mechanical Engineering
Vol. 39 No. 02 (2024): Proceedings of the Faculty of Technical Sciences
PREDICTIVE MODELING OF SURFACE ROUGHNESS DURING MILLING OF TITANIUM ALLOY Ti-6Al-4V USING MACHINE LEARNING
Fakultet tehničkih nauka, Departman za proizvodno mašinstvo
Abstract
Analyzing the influence of input factors on the output value (roughness), modeling the optimal combination of factors in order to achieve a better roughness of the processed surface.
References
[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.