Real-time monitoring and prognosis of energy consumption in hard milling

Z. Y. Liu, Michael P Sealy, Y. B. Guo, Z. Q. Liu

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

Abstract

Tool wear progression is inevitable in precision cutting. However, the effect of tool wear on energy consumption at machine, spindle, and process levels is yet to understand. In this study, specific energy in dry milling of hardened AISI H13 was studied at the machine, spindle, and process levels. The effect of process parameters and tool wear progression on energy consumption at each level was investigated. The results indicated that tool wear progression only has a predominant influence on energy consumption at the process level but not the machine and spindle levels. Energy consumption at machine level can be described with a traditional empirical model effectively. However, the traditional model is incapable of predicting energy consumption at the process level. The investigation in energy consumption at different levels can help improve energy efficiency. Since energy consumption at the process level is responsible for chip formation and surface generation, the study of energy consumption at this level is critical to understanding and optimizing of a machining process.

Original languageEnglish (US)
Title of host publicationInternational Symposium on Flexible Automation, ISFA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages422-427
Number of pages6
ISBN (Electronic)9781509034673
DOIs
StatePublished - Dec 16 2016
EventInternational Symposium on Flexible Automation, ISFA 2016 - Cleveland, United States
Duration: Aug 1 2016Aug 3 2016

Publication series

NameInternational Symposium on Flexible Automation, ISFA 2016

Other

OtherInternational Symposium on Flexible Automation, ISFA 2016
CountryUnited States
CityCleveland
Period8/1/168/3/16

Fingerprint

Milling (machining)
Energy utilization
Monitoring
Wear of materials
Energy efficiency
Machining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Liu, Z. Y., Sealy, M. P., Guo, Y. B., & Liu, Z. Q. (2016). Real-time monitoring and prognosis of energy consumption in hard milling. In International Symposium on Flexible Automation, ISFA 2016 (pp. 422-427). [7790200] (International Symposium on Flexible Automation, ISFA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISFA.2016.7790200

Real-time monitoring and prognosis of energy consumption in hard milling. / Liu, Z. Y.; Sealy, Michael P; Guo, Y. B.; Liu, Z. Q.

International Symposium on Flexible Automation, ISFA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 422-427 7790200 (International Symposium on Flexible Automation, ISFA 2016).

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

Liu, ZY, Sealy, MP, Guo, YB & Liu, ZQ 2016, Real-time monitoring and prognosis of energy consumption in hard milling. in International Symposium on Flexible Automation, ISFA 2016., 7790200, International Symposium on Flexible Automation, ISFA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 422-427, International Symposium on Flexible Automation, ISFA 2016, Cleveland, United States, 8/1/16. https://doi.org/10.1109/ISFA.2016.7790200
Liu ZY, Sealy MP, Guo YB, Liu ZQ. Real-time monitoring and prognosis of energy consumption in hard milling. In International Symposium on Flexible Automation, ISFA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 422-427. 7790200. (International Symposium on Flexible Automation, ISFA 2016). https://doi.org/10.1109/ISFA.2016.7790200
Liu, Z. Y. ; Sealy, Michael P ; Guo, Y. B. ; Liu, Z. Q. / Real-time monitoring and prognosis of energy consumption in hard milling. International Symposium on Flexible Automation, ISFA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 422-427 (International Symposium on Flexible Automation, ISFA 2016).
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