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Schneidera, U., Drusta, M., Ansalonib, M., Lehmannc, Ch., Pellicciarib, M., Lealib, F., Gunninkd, J.W. and Verla, A. (2016) Improving Robotic Machining Accuracy through Experimental Error Investigation and Modular Compensation. The International Journal of Advanced Manufacturing Technology, 85, 3-15.
https://doi.org/10.1007/s00170-014-6021-2

has been cited by the following article:

• TITLE: Error Prediction in Industrial Robot Machining: Optimization Based on Stiffness and Accuracy Limit

  AUTHORS: Yair Shneor, Vladimir Chapsky

  KEYWORDS: Robot Stiffness, Robot Machining Performances, Accuracy Prediction

  JOURNAL NAME: Engineering, Vol.13 No.6, June 30, 2021

  ABSTRACT: Among the advantages of using industrial robots for machining applications instead of machine tools are flexibility, cost effectiveness, and versatility. Due to the kinematics of the articulated robot, the system behaviour is quite different compared with machine tools. Two major questions arise in implementing robots in machining tasks: one is the robot’s stiffness, and the second is the achievable machined part accuracy, which varies mainly due to the huge variety of robot models. This paper proposes error prediction model in the application of industrial robot for machining tasks, based on stiffness and accuracy limits. The research work includes experimental and theoretical parts. Advanced machining and inspection tools were applied, as well as a theoretical model of the robot structure and stiffness based on the form-shaping function approach. The robot machining performances, from the workpiece accuracy point of view were predicted.