Active Form Error Control during Robotic Assisted Milling

Robotic milling is becoming increasingly popular, as an alternative to the use of conventional CNC (Computer Numerical Control) machines, due to the added dexterity, expansive working envelope and multi-station capability of robotic arms. However, one of the main issues that arises is the positional error that comes with using them and their low stiffness, compared to conventional industrial machines. There is therefore a great need to compensate these errors. Various methods have been investigated in the past in order to improve robotic milling errors, among which: robot command modification, manipulator model modification, optimisation of the existing robotic machining cell and the augmentation of the robotic machining cell. The concept of robotic assisted machining which was first proposed by Ozturk et al in conventional CNC milling is now gaining popularity as a viable solution to reduce form errors in robotic milling.

This study focusses on the use of a collaborative robot to mitigate form errors in both conventional CNC and robotic milling. The first setup is similar to the one proposed by Ozturk et al in peripheral milling, and the second setup is comprised of a milling robot and a colinear collaborative robot supporting the backface of the workpiece while the milling robot performs face milling.

The study begins with an in-depth analysis of cutting conditions, workpiece materials, and the various factors contributing to form errors in robotic milling. Simulations were conducted to model the performance of the proposed control methods under varying conditions, including robotic support forces, static stiffness, and position errors. Results from the simulations show that both force minimisation and thickness control significantly reduce form errors compared to traditional robotic milling approaches, with thickness control being particularly effective in mitigating errors across a range of scenarios. Under load ratings from the robot’s ball caster, the thickness control method achieved a 62% reduction in form error across rectangular paths when compared to unsupported milling trials, while the force minimisation method achieved a 9% decrease.

Experimental validation was conducted using a collaborative robot system equipped with a force sensor to measure form errors during milling trials. The experimental setup was carefully designed to benchmark the force control method against conventional robotic milling without error compensation. There was a decrease of 69% and 50% in form error in peripheral and pocket milling operations. The findings from both the simulation and experimental work demonstrate that integrating active form error control enhances machining precision, especially in challenging robotic milling tasks involving complex geometries and varying material properties.