Tehnički vjesnik, Vol. 28 No. 5, 2021.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20200614105300
Modelling and Optimization of Surface Roughness and Specific Tool Wear in Milling Process
Mehdi Heidari*
orcid.org/0000-0001-9703-2495
; Shahrood University of Technology, Faculty of Mechanical and Mechatronics Engineering, 7th Tir square, Shahrood, Iran, P. O. Box: 3619995161
Seyed Vahid Hosseini
; Shahrood University of Technology, Faculty of Mechanical and Mechatronics Engineering, 7th Tir square, Shahrood, Iran, P. O. Box: 3619995161
Hadi Parvaz
; Shahrood University of Technology, Faculty of Mechanical and Mechatronics Engineering, 7th Tir square, Shahrood, Iran, P. O. Box: 3619995161
Sažetak
The present study has been carried out to optimize three machining parameters in the milling process to achieve minimum surface roughness and tool wear along with the maximum material removal rate. A specific tool wear factor has been defined to evaluate both tool wear and material removal rate parameters simultaneously and the surface roughness was considered as the second output parameter. A set of experiments was designed using a DOE technique and conducted on a milling machine. The experimental data then was applied to develop different mathematical models and the best model was chosen based on analysis of variance (ANOVA). Three proposed methods of optimization with different natures were used to determine optimal output parameters based on selected models. The comparison between these methods showed that Regression-response optimization was superior to Simulated Annealing (SA) algorithm and Goal-attainment method. The Simulated Annealing (SA) algorithm also represented less error function compared to goal-attainment methods. The results of optimization revealed that optimum values for cutting speed and feed rate were ranged from 312 to 314 m/min and 0.085 to 0.12 mm/rev⸱tooth, respectively, while all optimization methods reached the same value of 1.0 mm for depth of cut parameter.
Ključne riječi
goal-attainment method; machining parameters; regression-response optimization; simulated annealing algorithm; specific tool wear; surface roughness
Hrčak ID:
261339
URI
Datum izdavanja:
15.8.2021.
Posjeta: 953 *