Energy analytics and machine learning modeling in milling of difficult-to-machine alloys

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dc.contributor Hu, Fei
dc.contributor Jiang, Zhe
dc.contributor Jordon, J. Brian
dc.contributor O'Neill, Zheng
dc.contributor Woodbury, Keith A.
dc.contributor.advisor Guo, Yuebin B.
dc.contributor.author Liu, Ziye
dc.date.accessioned 2019-08-01T14:23:50Z
dc.date.available 2019-08-01T14:23:50Z
dc.date.issued 2019
dc.identifier.other u0015_0000001_0003291
dc.identifier.other Liu_alatus_0004D_13738
dc.identifier.uri http://ir.ua.edu/handle/123456789/6104
dc.description Electronic Thesis or Dissertation
dc.description.abstract Manufacturing consumes approximately 20% of total energy consumption in the U.S. Energy consumption in machining, an important manufacturing cluster, is substantial, contributes to a large fraction of manufacturing cost, and has a significant environmental impact. On the other hand, energy consumption at the process level is directly related to surface integrity of machined part. Therefore, it is critical to investigate energy consumption in machining for process sustainability and surface integrity. This dissertation consists of seven chapters: (1) The first chapter gave a comprehensive literature review on energy consumption in machining; (2) The second chapter compared the characteristics of energy consumption in dry milling vs. flood milling of Inconel 718 superalloy at machine, spindle, and process levels; (3) The third chapter has developed an innovative holistic concept of cumulative energy demand to evaluate the energy demand in milling Inconel 718 from the life cycle perspective; (4) The forth chapter has proposed a hybrid machine learning model by integrating cutting mechanics to predict net cutting specific energy; (5) The fifth chapter has coined a new concept of energy-based process signature to establish the relationship between net cutting specific energy and surface integrity; (6) The sixth chapter has proposed a real-time energy consumption monitoring framework at machine and enterprise levels; and (7) The last chapter has summarized the conclusions and proposed the future work.
dc.format.extent 155 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Mechanical engineering
dc.title Energy analytics and machine learning modeling in milling of difficult-to-machine alloys
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Department of Mechanical Engineering
etdms.degree.discipline Mechanical Engineering
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.


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