Parameteric Optimization of CNC Milling Machine by using GSA-TLBO: A Comparitive Study
Keywords:
Milling machine, Al SiC, surface roughness, GSA, TLBAbstract
This work talks about the reduction in the surface roughness of the work surface by the use of a CNC milling machine. For this determination optimization algorithms are an option which gives a satisfactory set of values of CNC machine inputs which affect the roughness of the work surface during the milling operation. An aluminium metal matrix composites can be machined at high-speed conditions in CNC is sufficiently great because such composites have various applications in the aeronautics industry due to the requirement of good results. From the literature study, it is disclosed that most recent task is done on this by using Gravitational Search Algorithm (GSA) and is compared with TLBO, SA and GA (in order of their performance). By the use of mentioned techniques surface roughness value is obtained much satisfactory but there is always a scope of improvement. An experimental work done in the paper by Pare V et al., is considered as a reference to get the most favourable sets of input parameters of the CNC machine. Their demonstration provides an effective range of depth of cut, speed of cutting, step over ratio and feed. In this work, we have proposed a hybrid algorithm by cascading GSA and TLBO. GSA gives better results than TLBO but the convergence speed is lesser, so a combination of them will improve the result and speed. For this, we have developed the MATLAB script and compared the results with the paper. Results of our designed GSA and TLBO also perform well than listed in the paper. A comparison of results obtained by GSA-TLBO, GSA and TLBO is shown in our work and hybrid algorithm improved the surface roughness by 13.07% for linear analysis. The whole experiment is conducted on the Al + SiC metal matrix composite.
How to cite this article:
Sharma A. Parametric Optimization of CNC Milling Machine by using GSA-TLBO: A Comparative Study. J Adv Res Prod Ind Engg 2020; 7(3&4): 13-17.
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