This paper describes a novel methodology to reduce the effort in automating manual surface finishing processes by bridging the knowledge transfer gap of the manual operator's skills to a robot program. Key process variables (KPVs), i.e., contact force, tool path, and feed rate, of the manual operator performing the task are captured with a “sensorized” hand-held belt grinder, while the changes to the work-piece geometry is captured using a 3-D scanner. The entire manual tool-path strategy is segmented into its primitives or primary strategies before programming an equivalent robotic tool-path and strategy. The manual tool-path primitives are imported into computer-aided-manufacturing software where boundary splines are created to generate the robotic tool-paths. An analytical material removal rate (MRR) model is used to scale the extracted manual KPVs such that the parameters can be executed by the robotic platform, while still maintaining an equivalent material removal profile. In the first experimental trial with the designed robotic finishing strategy using this approach, the work-piece could be finished to within 0.7 mm of the desired shape.
['Wu Xin Ng', 'Hau Kong Chan', 'Wee Kin Teo', 'I-Ming Chen']
Programming a Robot for Conformance Grinding of Complex Shapes by Capturing the Tacit Knowledge of a Skilled Operator