Single-point metal turning processes can create chip nests that are hazards to both parts and machine tools. This is mitigated by a process called Modulated Tool Path (MTP) machining, which superimposes an oscillation in the tool tip feed direction in order to break these chips and provide an adequate surface finish. MTP machining is highly sensitive to the amplitude and frequency of this oscillation, both of which can often be diminished by standard machine tool controllers. These controllers are also unresponsive to iteration-varying disturbances such as temperature fluctuations, which can cause positional and velocity-related inaccuracies. This paper presents a library-based variant of Iterative Learning Control (ILC) called Disturbance and Performance-Weighted ILC (DPW-ILC), which is designed to improve the accuracy of machine tool trajectories that are highly oscillatory in nature, as well as provide robustness to varying, but measurable disturbances. DPW-ILC has been shown in simulation to provide a tremendous accuracy benefit over standard ILC techniques, specifically in the presence of two separate types of temperature-based disturbances.

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