The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, Data Dependent Systems (DDS), a stochastic modeling and analysis technique, was applied to study spindle motor energy consumption during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of Variance (ANOVA) was also used to determine factors significant to the energy consumption by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. Results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies (0.1 rad/sec) highlighted by DDS methodology.
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ASME 2018 13th International Manufacturing Science and Engineering Conference
June 18–22, 2018
College Station, Texas, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5137-1
PROCEEDINGS PAPER
Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear
Xingtao Wang,
Xingtao Wang
University of Nebraska-Lincoln, Lincoln, NE
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Robert E. Williams,
Robert E. Williams
University of Nebraska-Lincoln, Lincoln, NE
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Michael P. Sealy,
Michael P. Sealy
University of Nebraska-Lincoln, Lincoln, NE
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Prahalada Rao,
Prahalada Rao
University of Nebraska-Lincoln, Lincoln, NE
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Yuebin Guo
Yuebin Guo
University of Alabama, Tuscaloosa, AL
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Xingtao Wang
University of Nebraska-Lincoln, Lincoln, NE
Robert E. Williams
University of Nebraska-Lincoln, Lincoln, NE
Michael P. Sealy
University of Nebraska-Lincoln, Lincoln, NE
Prahalada Rao
University of Nebraska-Lincoln, Lincoln, NE
Yuebin Guo
University of Alabama, Tuscaloosa, AL
Paper No:
MSEC2018-6511, V003T02A002; 8 pages
Published Online:
September 24, 2018
Citation
Wang, X, Williams, RE, Sealy, MP, Rao, P, & Guo, Y. "Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear." Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference. Volume 3: Manufacturing Equipment and Systems. College Station, Texas, USA. June 18–22, 2018. V003T02A002. ASME. https://doi.org/10.1115/MSEC2018-6511
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