Abstract
In the present study, two mathematical models were developed to optimize the surface roughness for machining condition of Cedar of Lebanon pine (Cedrus libani). Taguchi approach was applied to examine the effect of CNC processing variables. Quality characteristics parameters were selected as arithmetic average roughness (Ra) and average maximum height of the profile (Rz) for wood material. Analysis of variance (ANOVA) was used to determine effective machining parameters. Developed mathematical models using response surface methodology (RSM) were optimized by a combined approach of the Taguchi’s L27 orthogonal array based simulated angling algorithm (SA). Optimum machining levels for determining the minimum surface roughness values were carried out three stages. Firstly, the desirability function wasused to optimize the mathematical models. Secondly, the results obtained from the desirability function were selected as the initial point for the simulated angling algorithm. Finally, the optimum parameter values were obtained by using simulated angling algorithm. Minimum Ra value was obtained spindle speed of 17377 rpm, feed rate of 2.012 m/min, tool radius of 8 mm and depth of cut of 2.009 mm by using desirability function based simulated angling algorithm. For Rz these results were found as 16980 rpm, 2.004 m/min, 8.001mm and 2.003 mm. The R-square values of the Ra and Rz were 95.91 % and 96.12 %, respectively. The proposed models obtained the minimum surface roughness values and provided better results than the observed values.
Highlights
Wood surface roughness is a crucial indicator of the quality of CNC processing parameters
Two mathematical models used for optimization of the surface roughness were explained
The Taguchi orthogonal array integrated with response surface methodology and simulated angling algorithm for solving these models were detailed
Summary
Wood surface roughness is a crucial indicator of the quality of CNC processing parameters. Wood material has very complex structure parameters such as machining procedure, physical properties of panel, processing conditions and anatomical structure (Magoss 2008, Philbin and Gordon 2006, Hiziroglu and Kosonkorn 2006, Ozdemir and Hiziroglu 2007, Ratnasingman and Scholz 2006, Hazir and Ozcan 2019). Design of experiment (DOE) and the Taguchi methodology are powerful techniques to reduce the number of experiments These methods have been commonly applied in different engineering applications (Yang and Tarng 1998, Taguchi et al 2005, Sarikaya and Güllü 2016, Rao and Murthy 2018, Selaimia et al 2017, Azhiri et al 2014, Majumder et al 2017, Kant and Sangwan 2014, Deepanraj et al 2017). Experimental design based metaheuristic algorithms have become popular method for optimizing the param-
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
