Multi Response Optimization Using Different Statistical Designs


Varalakshmi Medatati†, Rajya Lakshmi Kottapalli†, Charankumar Ganteda† and Giulio Lorenzini‡


† Department of Mathematics, Koneru Lakshmaiah Education Foundation (KL deemed to be University), Greenfields, Vaddeswaram, Guntur- 522502, India.
‡ Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy.

Corresponding Author Email: [email protected]

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Statistical methods provide an optimal solution for solving the problems with a few experiments to reduce the cost and time of the experimentation. In the present work, three statistical methods namely Taguchi’s designs, central composite designs (CCD) and balanced incomplete block design (BIBD) have been considered. The adequacy of these methods is examined considering the test data Force and Power in Chopping agricultural residues. The existing data is not sufficient for testing the adequacy of these statistical methods, an empirical relation is developed from the test data by the method of response surface methodology (RSM). From the developed empirical relation, the necessary data is generated for CCD and BIBD. Empirical relations are also developed with data of the test runs in CCD and BIBD. The optimal parameters are identified from these methods and verified with test data. The best approach is identified on the basis of number of experiments and adequacy in estimating the performance indicator for the identified optimal input variables. The minimum force and power estimated for the optimal input variables with the help of Taguchi approach is close to the test results. Estimates from CCD and BIBD are slightly low to that of test results.