Development of an automated modal extraction methodology through OMA by random cutting excitation of a legacy milling machine

The quality of the final product of machining is strongly dependent on our ability to understand, control and suppress the harmful interplay between the machine and machining which leads to chatter. Therefore extraction of the dynamic information of the machine tool is of utmost importance. Unfortunately, these dynamic parameters are known to be varying between static test condition and operational cutting condition. Operational Modal Analysis (OMA) is a suite of powerful output only analysis techniques which obviates the above problem by performing modal extraction during actual cutting operation. This work uses a novel workpiece design which when flat milled, produces the necessary broadband white exciting force input for the milling machine. The present work also proposes a novel iterative technique which discriminates between structural response modes and tooth passing harmonics which are inherently present due to the rotating spindle. The harmonic indicator does this detection by exploiting the separation in response of the feed and transverse directions to form a differenced mean power discriminator spectrum. Performance of this indicator against a few other well-known discriminants is compared. Thereafter, the complete automated modal decomposition methodology is applied on a legacy vertical milling machine to assess the efficacy of the proposal. (C) 2018 Elsevier Ltd. All rights reserved.