Superior optimal inverse filtering of cutting forces in milling of thin-walled components. (January 2023)

• Record Type: Journal Article
• Authors: Totis, G.; Sortino, M.
• Is Part Of: Measurement. Volume 206(2023)
• Publication Date: 2023-01
• Subjects: Milling -- Cutting forces -- Dynamometer -- Dynamics -- Compensation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112227
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Measuring the cutting force when milling slender/thin-walled parts is difficult because of the large and long-lasting structural vibrations that cause inertial disturbances in the measured signals. Under these conditions, signal filtering is the only option to significantly extend the dynamic bandwidth of the device above 3 kHz. Non-parametric filters are typically preferred over parametric ones because they are more practical and easier to apply in industrial applications. Currently available parametric filters cannot address this problem because they are based on oversimplified transmissibility models or are affected by computational problems when the impulse responses of the device are excessively long. In this study, the novel non-parametric Superior Optimal Inverse Filter was developed to address the limitations of state-of-the-art filters. It is a non-trivial extension of the Optimal Inverse Filter to a higher dimensionality, and it can process long transients and generic (possibly aperiodic) signals. Thus, outstanding results were obtained both from modal analysis and from an actual case study, demonstrating the potential of the new filter for an effective and almost completely automatic cutting force dynamic compensation when milling thin-walled structures. The proposed filter was compared with parametric Kalman filters and with the existing non-parametric filters, and it offered a considerably better performance, particularly for compensating for cross disturbances and for input force position-dependent dynamics. Graphical abstract: Highlights: State of the art filters fail cutting force compensation in milling of thin-walled parts. Novel filter is non-parametric and dramatically more computationally efficient. Tool position-dependent dynamometer dynamics are fully 3D-compensated. All kind of cutting force signals can be filtered, not only periodic regimes. Better characterization of cutting mechanics and tool–workpiece engagement is achieved.