PARAMETRIC SELECTION OF INDUSTRIAL ROBOTS USING REDUCED PCR/PLSR MODELS FOR BETTER ESTIMATES OF EXPECTED COST AND SPECIFICATIONS

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Published: Dec 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i9.4815
Keywords:
Industrial Robot, Robot selection, Robot parameters, Partial Least Square Regression (PLSR), Principal Component Regression (PCR), MSE, RSE, RMSE

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Sasmita Nayak
Ph.D Research Scholar, Suresh Gyan Vihar University, Jaipur, India
B. B. Choudhury
Dept. of Mech. Engg., IGIT, Sarang, Odisha, India

Abstract

Quick advancement of industrial robots along with its usage by the assembling industries for various applications is a basic assignment for the determination of robots. As an outcome, the choice procedure of the robot turns out to be particularly entangled for the potential users since they have a broad arrangement of parameters of the accessible robots. In this paper, Partial Least Square Regression (PLSR) and Principal Component Regression (PCR) algorithm are utilized for the selection of industrial robots. Through this proposed technique utilizes eleven parameters which are directly considered as inputs for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot estimates from the perfectly the best probable robot which specifies the majority genuine benchmark of robot selection for the particular application by means of the proposed algorithm. Furthermore, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error(RSE), as well as Root Mean Square Error (RMSE).

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Vol. 8 No. 9 (2017): NOVEMBER-DECEMBER 2017
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