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  Scopus ID: 21100926589

Application of Sensitivity Analysis on Regression Models for Assessing Optimal Carbon Emissions from CNC Manufacturing Systems

Durga Venkata Prasad Ramena, Arun Vikram Kothapalli, Suresh Kilaparthi and Satya Sankara Srinivas Rao Maruvada

Abstract

Machining industries always strive to show less hazardous impacts, such as carbon emissions and resource wastages, in overall global emissions. To achieve better reduction of pollution from machining industries, they prefer to utilize the concepts of optimization and stability studies. Hence, this present work deals with determining the sources of carbon emissions from various routes of CNC manufacturing systems, such as CNC lathe and turn-mill center operations and assessing them analytically under dry and wet (aerosol-mist of green fluid such as neem oil) conditions. The analysis of the emission results is used to get an insight into the effect of the chosen manufacturing system on the environment. The impact of the machining parameters (such as cutting speeds, tool feeds, and depth of cuts) in generating the emissions is also studied for optimization. The consistency in stability of the optimality is thus analyzed using sensitivity analysis based on the empirical regression models. The design of experiments is based on Taguchi orthogonal array theory, and the individual optimization is done using the concept of signal-to-noise ratios.

Published on: November 23, 2023
doi: 10.17756/nwj.2023-s4-004
Citation: Ramena DVP, Kothapalli AV, Kilaparthi S, Maruvada SSSR. 2023. Application of Sensitivity Analysis on Regression Models for Assessing Optimal Carbon Emissions from CNC Manufacturing Systems. NanoWorld J 9(S4): S20-S27.

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