Contribution of Surface Finish Monitoring Signals in CNC Taper Turning

Eustaquio García Plaza; Pedro José Núñez; David Rodríguez Salgado; Inocente Cambero Rivero; José María Herrera Olivenza; Justo García Sanz-Calcedo

doi:10.4028/www.scientific.net/MSF.797.41

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Comparison of Diameter and Area Change Based Methods for Evaluating Break-IN and Break-OUT Damages in Dry Drilled Holes of Aeronautical Carbon Fiber Composites
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Cutting Forces Prediction in the Dry Slotting of Aluminium Stacks
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HomeMaterials Science ForumMaterials Science Forum Vol. 797Contribution of Surface Finish Monitoring Signals...

Contribution of Surface Finish Monitoring Signals in CNC Taper Turning

Abstract:

On-line monitoring systems eliminate the need for post-process evaluation, reduce production time and costs, and enhance automation of the process. The cutting forces, mechanical vibration and acoustic emission signals obtained using dynamometer, accelerometer, and acoustic emission sensors respectively have been extensively used to monitor several aspects of the cutting processes in automated machining operations. Notwithstanding, determining the optimum selection of on-line signals is crucial to enhancing system optimization requiring a low computational load yet effective prediction of cutting process parameters. This study assess the contribution of three types of signals for the on-line monitoring and diagnosis of the surface finish (Ra) in automated taper turning operations. Systems design were based on predictive models obtained from regression analysis and artificial neural networks, involving numerical parameters that characterize cutting force signals (F_x, F_y, F_z), mechanical vibration (a_x, a_y, a_z), and acoustic emission (EA_RMS).

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Periodical:

Materials Science Forum (Volume 797)

Pages:

41-46

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.797.41

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Online since:

June 2014

Authors:

Eustaquio García Plaza*, Pedro José Núñez, David Rodríguez Salgado, Inocente Cambero Rivero, José María Herrera Olivenza, Justo García Sanz-Calcedo

Keywords:

Artificial Neural Network (ANN), Monitoring, Regression Model, Surface Finish, Turning CNC

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