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dc.creatorMesina, Omez Samoon
dc.date.accessioned2012-06-07T22:32:59Z
dc.date.available2012-06-07T22:32:59Z
dc.date.created1993
dc.date.issued1993
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-1993-THESIS-M578
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.en
dc.descriptionIncludes bibliographical references.en
dc.description.abstractMetal cutting operations constitute a majority of all manufacturing activities. The detection of tool wear is rudiment to the smooth functioning of the metal cutting operation. In this thesis, the complex yet essential task of deriving a model for the detection of tool wear is done using a neuro-fuzzy system. The neural network captures the steady state relationship between the condition of the tool and sensor values, in the weights of the connections between neurons. The fuzzy rules and the entire mechanism are used to provide a linguistic model for the detection of tool wear. However the fuzzy membership functions need to be tuned so that they reflect the true meaning of the process variables. This is done by using an error-based, density-driven adaptation scheme. The successful prediction of tool wear in a milling process by using the neuro-fuzzy system with the intrinsic adaptation scheme is demonstrated.en
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherTexas A&M University
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.subjectmechanical engineering.en
dc.subjectMajor mechanical engineering.en
dc.titleA neuro-fuzzy system for tool condition monitoring in metal cuttingen
dc.typeThesisen
thesis.degree.disciplinemechanical engineeringen
thesis.degree.nameM.S.en
thesis.degree.levelMastersen
dc.type.genrethesisen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen


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