Analytical force and dynamic models for material removal processes such as end and face milling do not account for material and process related uncertainties such as tool wear, tool breakage and material inhomogeneity. Optimization of material removal processes thus requires not only optimal process planning using analytical models but also on-line monitoring of the process so that adjustments, if needed, can be initiated to maximize the productivity or to avoid damaging expensive parts. In this thesis, a Polyvinylidene Fluoride (PVDF) sensor based process monitoring method that is independent of the cutting conditions and workpiece material is developed for measuring the cutting forces and/or torque in milling. The research includes the development of methods and hardware for wireless acquisition of time-varying strain signals from PVDF sensor-instrumented milling tools rotating at high speeds and transformation of the strains into the measurand of interest using quantitative physics-based models of the measurement system. Very good agreement between the measurements from the low cost PVDF sensors and the current industry standard, piezoelectric dynamometer, has been achieved. Three PVDF sensor rosettes are proposed for measuring various strain components of interest and are shown to outperform their metal foil strain gauge counterparts with significantly higher sensitivity and signal to noise ratio. In addition, a computationally efficient algorithm for milling chatter recognition that can adapt to different cutting conditions and workpiece geometry variations based on the measured cutting forces/torque signals is proposed and evaluated. A novel complex exponential model based chatter frequency estimation algorithm is also developed and validated. The chatter detection algorithm can detect chatter before chatter marks appear on the workpiece and the chatter frequency estimation algorithm is shown to capture the chatter frequency with the same accuracy as the Fast Fourier Transform (FFT). The computational cost of the chatter detection algorithm increases linearly with data size and the chatter frequency estimation algorithm, with properly chosen parameters, is shown to perform 10 times faster than the FFT. Both the cutting forces/torque measurement methodology and the chatter detection algorithm have great potential for shop floor application. The cutting forces/torque measurement system can be integrated with adaptive feedback controllers for process optimization and can also be extended to the measurement of other physical phenomena.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/47714 |
| Date | 05 February 2013 |
| Creators | Ma, Lei |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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