SAMP Rao algorithm based minimization of the roughness of milled surface of Ti-6Al-4V

Abstract

In order to solve optimization problems including machining responses as objectives, this study suggests a parameter-less method called the self-adaptive multi population (SAMP) Rao algorithm that does not rely on metaphors. When machining titanium alloys, achieving a good surface quality is a difficult process. In the current study, an effort has been made to reduce surface roughness during milling Ti-6Al-4V. Response surface methodology (RSM) was used in the experiment design to create a model for surface roughness using cutting parameters as variables. The developed model was tested in additional tests in addition to the primary experiments. It was shown that cutter speed and feed rate had the biggest effects on milled roughness, whereas depth of cut had very little of an impact. The model’s quality is demonstrated by the correlation coefficient (R2) 98%, which indicates that the model can explain 98% of the data. Later, a response surface-based desirability technique was used to minimize roughness of milled surface. The outcome of the proposed algorithm is compared with RSM optimizer. It has been noted that the outcomes achieved with the SAMP approach are more advantageous than RSM approach and found 18% minimum value of roughness of milled surface. Main reason to get better result in case of SAMP Rao algorithm is that it is based on the iterative search process with random values of cutting parameters with in defined range. SAMP Rao Algorithm provides cutting settings of 133.5 m min−1, 0.13 mm/tooth feed rate, and 2.06 mm of milling depth along with a minimal roughness of milled surface of 0.37 μm.