Abstract:Purpose: Development of industrial robots and its usage by different manufacturing industries in lot many diverse applications is a very much serious task for the selection of robots. As a consequence, the selection process of the robot becomes incredibly complicated for the potential users because they have a large set of attributes and parameters of the robots are available at their disposal. Moreover, the proposed work gives the efficient decision for the selection of the robots to the potential users of the robots or robot manufacturer.Design/methodology/approach: In this paper, it has been proposed that the scaled conjugate gradient fastest back-propagation algorithm used for the optimized way of selection of robot based on the appropriate parameters. Findings:The ranks of the desired industrial robots are evaluated using the proposed algorithm from the entire best possible robot that specifies the most genuine yardstick of robot selection for the particular application. The complete design performance of the proposed method discussed with different prediction errors such as mean square error, root mean square error, and R-squared error. Originality/value:The proposed methodology is an original scientific work and the algorithm used is an efficient algorithm for the selection of industrial robot. In this work, ten numbers of parameters of the robot are used for the selection of the industrial robot and eight broad categories of the robot are proposed called robot-rank.

No abstract

No 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. In this proposed technique, eleven different parameters are taken as direct inputs for selecting a robot as compared to those of the existing models, which are limited up to seven parameters. Basing upon the proposed algorithm, the rank of an industrial robot is estimated. Moreover, the best robot that has been selected should satisfy the benchmark genuinity for a targeted application. In addition to this, the robot selection algorithm is measured through Mean Square Error (MSE), and Root Mean Square Error (RMSE), R-squared error(RSE).

No abstract