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Prediction of workpiece elastic deflections under cutting forces in turning

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dc.contributor.author Benardos, PG en
dc.contributor.author Mosialos, S en
dc.contributor.author Vosniakos, GC en
dc.date.accessioned 2014-03-01T01:24:52Z
dc.date.available 2014-03-01T01:24:52Z
dc.date.issued 2006 en
dc.identifier.issn 0736-5845 en
dc.identifier.uri http://hdl.handle.net/123456789/17487
dc.subject elastic deflection en
dc.subject cutting forces en
dc.subject CNC turning en
dc.subject elastic line en
dc.subject artificial neural networks en
dc.subject.classification Computer Science, Interdisciplinary Applications en
dc.subject.classification Engineering, Manufacturing en
dc.subject.classification Robotics en
dc.subject.other UNIFIED-GENERALIZED MECHANICS en
dc.subject.other ERROR COMPENSATION en
dc.subject.other OPERATIONS en
dc.subject.other MODEL en
dc.title Prediction of workpiece elastic deflections under cutting forces in turning en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.rcim.2005.12.009 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.rcim.2005.12.009 en
heal.language English en
heal.publicationDate 2006 en
heal.abstract One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel(C) workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions. (C) 2006 Elsevier Ltd. All rights reserved. en
heal.publisher PERGAMON-ELSEVIER SCIENCE LTD en
heal.journalName ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING en
dc.identifier.doi 10.1016/j.rcim.2005.12.009 en
dc.identifier.isi ISI:000240228600013 en
dc.identifier.volume 22 en
dc.identifier.issue 5-6 en
dc.identifier.spage 505 en
dc.identifier.epage 514 en


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