Abstract
This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the same experimental model. In both cases, work pieces (probes) of the same material were machined (steel and aluminum probes) with cutting parameters and characteristics proper of production processes in industries such as aeronautics and automotive. The main objective of this study was to compare surface quality prediction models created in two machining centers to establish differences in outcomes and the possible causes of these differences. In addition, this paper deals with the validation of each model concerning surface quality obtained, along with comparing the quality of the models with other predictive surface quality models based on similar techniques.
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Acknowledgments
The authors want to thank the collaboration of Nicolás Correa S.A for the use of the M-Versa machining center made in the company, particularly thank Dr. Andrés Bustillo from Nicolás Correa S.A., who enabled experimentation in the company. The authors also thank the Centro de Automática y Robótica at CSIC (Spain), where the rest of the experimentation was made using Kondia machine-tool, as part of the team from Ghame group belonging to this center.
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Flores, V., Correa, M., Quiñonez, Y. (2017). Performance of Predicting Surface Quality Model Using Softcomputing, a Comparative Study of Results. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_23
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