MULTIRESPONSE OPTIMIZATION OF AGRICULTURAL RESIDUES USING MODIFIED TAGUCHI APPROACH AND STATISTICAL CONDITIONING OF THE DATA

Abstract

Unavoidable scatter in repeated test results can be due to influence of unknown process variables (if any) and measurement errors to a certain extent. Influence of the output is due to the noise in the experimental data and measurement error. Statistical conditioning of data is useful to consolidate the data. Modified Taguchi approach recommends few tests as per the orthogonal array and provides the range of estimates for combinations among the levels of process variables. It identifies optimal process variables and demands additional experimentation for confirmation (if necessary). One of the widely applied Chopping processes (namely, Agricultural residues) utilizes Cutting Force and Cutting Power under different conditions. This article presents optimal process variables for Cutting Force and Cutting power in Chopping agricultural residues adopting multi-objective optimization. Empirical relations are presented for Cutting Force and Cutting power in terms of Chopping process of corn stalk variables. Comparative study indicates reasonably good agreement between empirical relations and test results. Since the data is insufficient for the test runs demanded by the Taguchi approach, an empirical relation is developed from the test data using the response surface methodology (RSM).