Performance analysis of accuracy and repeatability of IRB1410 industrial robot using taguchi analysis with machine learning approach

Industrial robots are used for various industrial applications which requires the high level of accuracy of the movement and repeatability of the operations in the shop floor. To maintain the accuracy, it is very important to measure the positional error of the robot by using a dial gauge indicator. The noise factors like Environmental conditions, machine wear, friction between parts and calibration issues may influence the repeatability and accuracy of the robot. This research is designed to evaluate and analyse the accuracy and repeatability of IRB1410 Robot using L9 Taguchi design of experiments. The major influencing parameters of robot being manoeuvring speed, the distance of movement and payload are optimized which affects the accuracy and repeatability of the robot. A Robotic simulation is carried out to check the maneuvering ability of the robot by rapid programming on a controller to run it on a fixed path, a linear motion, on a virtual platform and check the variability of the setup of the experiments. The stability of the motion of the robot is experimented using tools like dial indicator in industrial robot itself. A carefully repeated motion is fed into the robot using Flex pendant controller to fulfil the purpose of testing the repeatability and accuracy of the industrial robot. The ANOVA analysis is carried out to obtain the most influencing robot parameters on the accuracy of the Robot. A statistical analysis of robot position error is analysed by using a polynomial regression model of machine learning. The speed of the robot is a major influencing parameter of robot positional error with less than 5%.