Abstract
Modelling and optimization of machining process is essential in order to avoid the laborious experiments and associated expenditure. The sparse data of the machining process is often used to model the mechanical activity and further the same are used to optimize the process. Components are machined to achieve better dimensional accuracy and surface roughness. Several systems are in existence to predict the dimensional accuracy of the components, whereas surface roughness estimation systems are rare. To achieve desired surface roughness operators needs experience in setting operating parameters such as speed (s), feed (f) and depth of cut (d). In this work, a combine flower pollination algorithm based forward mapping (prediction model) and operator friendly reverse mapping (optimization) models are developed. In prediction model, given the operation parameters it could predict the surface roughness and in optimization model, the operator will input desired surface roughness to achieve the optimized the operating parameters. As such operator friendly reduces the costly trial and error experiments, time, raises the productivity and level of automation in machining industry. The model development and optimization are carried out in Matlab® and the results are demonstrated.
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Umamaheswara Raju, R.S., Chakravarthy, V.V.S.S.S. & Chowdary, P.S.R. Flower pollination algorithm based reverse mapping methodology to ascertain operating parameters for desired surface roughness. Evol. Intel. 14, 1145–1150 (2021). https://doi.org/10.1007/s12065-021-00574-1
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DOI: https://doi.org/10.1007/s12065-021-00574-1