Modeling of robotic machining processes

• Main Author: Park, Chang Beom
• Format: Others
• Language: English
• Published: 2012
• Subjects: Robotic machining; Visual maps
• Online Access: http://hdl.handle.net/2152/ETD-UT-2012-08-6069

Many high value-machining processes such as milling and drilling have been performed by expensive and dedicated (single purpose) machine tools including CNC machine tools. Industrial robots are a good alternative to these conventional dedicated machine tools due to the robots’ many advantages such as lower cost, larger workspace, higher flexibility of motion, and versatility. Despite these advantages, several barriers prevent them from being widely adopted for high value machining purposes. Two of these barriers are low and nonlinear stiffness of the industrial robot arm and the manufacturing end-users not knowing the capabilities and advantages of robots in machining applications. This research sets out to help a typical machining operator who is not an expert in robotics to learn the capability of a given robotic machining system. This study should help the operator plan robotic machining processes by presenting process models and visual maps for a variety of machining processes and workpiece materials. The study shows in particular how the cutting force and the compliance of a robotic machining system affect machining processes. To meet this objective, we present a framework for planning development for any given robotic machining application domain. First, we select primary performance parameters (including joint torque limit and end-effector positional error) and control parameters (including machining parameters, end-effector position, and workpiece position) for robotic machining. Then, we present the system models and visual performance maps for the functional parameters of robotic machining processes. The focus is on cutting forces for the ten selected machining processes and end-effector positional error of a robotic machining system due to the compliance of a robotic system (i.e., robot manipulator and cutting tool) and joint error (due to sensor error and gear backlash). Finally, we present five applications to show how to use visual maps for preliminary planning scnearios of robotic machining processes. The applications present a step-by-step process for selecting from cutting parameters to workpiece position parameters by utilizing performance requirements and visual maps developed in this research. === text