Scaled conjugate gradient back-propagation algorithm for selection of industrial robots

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 purely original scientific work and very fastest algorithm for the selection of industrial robot. In this work maximum, 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.