Identification of machine tool dynamics under operational conditions

Chatter stability prediction is essential to improve productivity of modern milling process, especially at high speed. However, predictive models suffer reduced accuracy at high speed caused by input data inaccuracies like, for example, machine tool dynamics that is acquired in a stationary configuration but it changes with spindle speed. In this paper an efficient method to identify speed-varying FRF under operational conditions is presented. Proposed approach is based on experimental chatter limits (i.e., chatter frequency and related depth of cut), obtained by an efficient test, called Spindle Speed Ramp-up. Experimental results are then combined with analytical stability solution. By minimizing differences between experimental and predicted chatter conditions, a dedicated algorithm computes speed-varying FRF robustly and efficiently. Few tests and simple equipment (i.e., microphone) are enough to calculate FRF in a wide range of spindle speed. Proposed technique was validated in real machining scenario, speed- varying FRF identified is in line with expected trend found in literature.