Tool-path generation and optimization for robot machining using Artificial Intelligence

This research aims to generate and optimize optimal tool-path(process path of robot) by learning data on kinematics and dynamics properties required for the machining process using multi-axis robots with Artificial Intelligence(AI). Based on the end-effector of the robot, a new approach is proposed to optimize tool path in drilling or milling processes and to develop AI algorithms in terms of surface roughness, circularity, defects, and time. Recently, the manufacturing robot market has been gradually expanding and research has been actively conducted. Accurate prediction and process optimization are essential for the machining process. However, most industrial robots do not provide information about the robot’s dynamic parameters. For precision of machining requires data on dynamic properties that affect the result. Therefore, researches are being carried out to estimate dynamic properties. But, these methods take a long time and are very complex. Thus, if this research is carried out, it is expected that the outcomes of the development of new analysis methods for robot machining will be expected through the development of AI algorithms.

Faculty Supervisor:

Jihyun Lee

Student:

Partner:

Ulsan National Institute of Science and Technology (UNIST)

Discipline:

Engineering

Sector:

Artificial Intelligence; Advanced Manufacturing; Other

University:

University of Calgary

Program: