Stochastic modeling and analysis of spindle energy consumption during hard milling with a focus on tool wear

Xingtao Wang, Robert E. Williams, Michael P Sealy, Prahalada Rao, Yuebin Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationManufacturing Equipment and Systems
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791851371
DOIs
StatePublished - Jan 1 2018
EventASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 - College Station, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
Volume3

Other

OtherASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
CountryUnited States
CityCollege Station
Period6/18/186/22/18

Fingerprint

Energy utilization
Wear of materials
Monitoring
Analysis of variance (ANOVA)
Sustainable development
Time series
Machining
Systems analysis
Processing
Experiments

Keywords

  • ANOVA
  • Dds
  • Energy consumption
  • Frequency
  • Tool wear

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Wang, X., Williams, R. E., Sealy, M. P., Rao, P., & Guo, Y. (2018). Stochastic modeling and analysis of spindle energy consumption during hard milling with a focus on tool wear. In Manufacturing Equipment and Systems (ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018; Vol. 3). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/MSEC2018-6511

Stochastic modeling and analysis of spindle energy consumption during hard milling with a focus on tool wear. / Wang, Xingtao; Williams, Robert E.; Sealy, Michael P; Rao, Prahalada; Guo, Yuebin.

Manufacturing Equipment and Systems. American Society of Mechanical Engineers (ASME), 2018. (ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018; Vol. 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, X, Williams, RE, Sealy, MP, Rao, P & Guo, Y 2018, Stochastic modeling and analysis of spindle energy consumption during hard milling with a focus on tool wear. in Manufacturing Equipment and Systems. ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018, vol. 3, American Society of Mechanical Engineers (ASME), ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018, College Station, United States, 6/18/18. https://doi.org/10.1115/MSEC2018-6511
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. In Manufacturing Equipment and Systems. American Society of Mechanical Engineers (ASME). 2018. (ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018). https://doi.org/10.1115/MSEC2018-6511
Wang, Xingtao ; Williams, Robert E. ; Sealy, Michael P ; Rao, Prahalada ; Guo, Yuebin. / Stochastic modeling and analysis of spindle energy consumption during hard milling with a focus on tool wear. Manufacturing Equipment and Systems. American Society of Mechanical Engineers (ASME), 2018. (ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018).
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