Increasing Part Geometric Accuracy in High Speed Machining using Cascade Iterative Learning Control

High speed machining provides high productivity and low cycle times. Post machining, there can exist differences between desired measured part geometry due to tool deflection induced from higher feedrates. Reducing the feedrate leads an increase in time. Using predicted drive responses on a virtual CNC with integrated surface location error model, this research is first time Iterative Learning Control (ILC) has been applied reduce errors deflection. Validation trials demonstrated that ILC scheme improved performance whilst maintaining times when compared baseline program.