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A sensor module that integrates a thin film polyvinylidene fluoride (PVDF) piezoelectric strain sensor and an in situ data logging platform has been designed and implemented for monitoring of the feed and transverse forces in the peripheral end milling process. The module, which is mounted on the tool shank, measures the dynamic strain(s) produced in the tool and logs the data into an on-board card for later retrieval. The close proximity between the signal source and the PVDF sensor(s) minimizes the attenuation and distortion of the signal along the transmission path and provides high-fidelity signals. It also facilitates the employment of a first principles model based on the Euler–Bernoulli beam theory and constitutive equations of the piezoelectric sensor material to relate the in situ measured PVDF sensor signals to the feed and transverse forces acting on the tool. The PVDF sensor signals are found to compare well with the force signals measured by a platform-type piezoelectric force dynamometer in peripheral end milling experiments.

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A new method for online chatter detection and suppression in robotic milling is presented. To compute the chatter stability of robotic milling along a curvilinear tool path characterized by significant variation in robot arm configuration and cutting conditions, the tool path is partitioned into small sections such that the dynamic stability characteristics of the robot can be assumed to be constant within each section. A methodology to determine the appropriate section length is proposed. The instantaneous cutting force-induced dynamic strain signal is measured using a wireless piezoelectric thin-film polymer (polyvinyldene fluoride (PVDF))-based sensor system, and a discrete wavelet transform (DWT)-based online chatter detection algorithm and chatter suppression strategy are developed and experimentally evaluated. The proposed chatter detection algorithm is shown to be capable of recognizing the onset of chatter while the chatter suppression strategy is found to be effective in minimizing chatter during robotic milling.

This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages and for chatter frequency estimation by monitoring the cut-ting force signals. Based on a complex exponentials model for the dynamic chip thick-ness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the method is shown to be capable of distinguish-ing between chatter and the dynamic transients in the cutting forces arising from sudden changes in workpiece geometry and tool entry/exit. To facilitate chatter suppression once the onset of chatter is detected, a time domain algorithm is proposed so that the dominant chatter frequency can be accurately determined without using computationally expensive frequency domain transforms such as the Fourier transform. The proposed method is experimentally validated. [DOI: 10.1115/1.4023716]

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