Posture optimization in robotic drilling using a deformation energy model

Jae Ho Lee, Seong Hyeon Kim, Byung Kwon Min

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Robots have various advantages such as multiple degrees of freedom (DOFs), flexibility, and cost efficiency. Although robots have mostly been utilized for painting, welding, pick-and-place, and assembly tasks, robot machining has been actively introduced in many fields to maximize productivity. The main obstacle to robot machining is the poor stiffness of robots, which is lower than that of conventional machine tools. Low stiffness induces compliance errors that deteriorate machining quality. One effort to improve stiffness is tool path compensation, in which compliance errors are predicted and compensated for by utilizing a stiffness model. Another approach to solving the stiffness problem is posture optimization, in which a performance index is defined based on the stiffness model. Such a performance index evaluates stiffness according to the configuration of a robot. Using this index, one can optimize the posture of a robot to maximize stiffness during machining. In previous studies, optimization was performed for the redundant DOF in the spindle axis direction at a given position, assuming a fixed workpiece and vertical feed direction. However, workpiece placement also has to be optimized in the robot workspace to obtain the optimal machining posture. This study introduced a novel approach to posture optimization. A deformation energy model that can evaluate stiffness from an energy perspective was proposed. With the proposed method, the posture of the robot and the workpiece position can be optimized simultaneously. Global optimization was performed within the entire workspace, and simulations were conducted to verify the optimization results. Additionally, local optimization considering the working environment was performed to deal with the practical problems in real scenarios. The results of optimization were verified experimentally.

Original languageEnglish
Article number102395
JournalRobotics and Computer-Integrated Manufacturing
Volume78
DOIs
Publication statusPublished - 2022 Dec

Bibliographical note

Funding Information:
This work was supported by the Technology Development Program for Smart Controller in Manufacturing Equipment ( 20012834 , Development of Smart CNC Control System Technology for Manufacturing Equipment) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea)

Publisher Copyright:
© 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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