Abstract
All machining processes involve vibrations generated by structural sources such as a machine’s moving parts or by the interaction between cutting tools and work-pieces. Relative vibrations between the work-pieces and the cutting tool are the most relevant from the point of view of the regenerative chatter phenomenon. In fact, these vibrations can lead to a chip yregeneration effect, which results in unwanted consequences, rapidly degenerating towards a very poor quality of surface finishing or, in case of severe chatter conditions, to machine-tool or work-piece damage. In the past decades, two different approaches for chatter avoidance were proposed by the scientific community, and they are commonly referred to as Out-of-Process (OuP) and in-Process (iP) solutions. The OuP solutions are off-line approaches, which allow to properly set the working parameters before machining starts. Ip solutions are on-line techniques, which allow to dynamically change the working parameters during machining by using single or multiple sensors. By monitoring the machining process, iP algorithms try to keep the machining process in stable working conditions while keeping high productivity levels. This study dealt with a novel iP chatter-detection strategy based on the Power Spectral Density (PSD) analysis and on the Wavelet Packet Decomposition (WPD) of different sensor signals. The preliminary results demonstrate the stability and feasibility of proposed indicators for chatter detection in industrial application.
Highlights
Metal-cutting processes, such as milling, turning, and drilling, are subject to vibration phenomena, originating from their transient and unpredictable nature and from the limited— usually very high—stiffness of the machine structure, including the work-piece fixture [1]
We proposed two iP algorithms for chatter detection based on the calculation of two chatter indicators, based on the Power Spectral Density (PSD-iP-Chatter Indicator (CI)) and the Wavelet Packet Decomposition (WPD-iP-CI) analysis of the monitored signals, respectively
This feature could represent a promising approach for real-time chatter detection using an embedded platform for industrial applications
Summary
Metal-cutting processes, such as milling, turning, and drilling, are subject to vibration phenomena, originating from their transient and unpredictable nature and from the limited— usually very high—stiffness of the machine structure, including the work-piece fixture [1]. During particular combinations of the spindle speed and the feed rate, it may happen that the relative vibration between the cutting tool and the work-piece will create a chip thickness modulation synchronised, in phase, between two successive cutter passages causing the chip thickness alterations at every tool passage Whenever chatter occurs, other dynamic events will prevail, leading to a dominance of a set of aperiodic components, meaning that the vibrations sparking the chatter phenomena are not resulting from the cutting process periodicity itself but mostly to a dynamic interaction in the machine–tool–workpiece structural chain This considerations motivate the well-established approach [17,28,29] according to which a generic signal s(t), irrespective of the sensor type and of the location on the machine or its vicinity, results from the superposition of a periodic component sp(t), an aperiodic component sa(t), and noise sn(t): s(t) = sp(t) + sa(t) + sn(t). Within the scope of this work, we used for both the analysis approaches a window of a half second, corresponding to a frequency resolution of 2 Hz
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