Performance of predicting surface quality model using softcomputing, a comparative study of results

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 s...

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Autores:
Correa Valencia, Maritza
Flores, Víctor
Quinonez, Alma Yadira
Tipo de recurso:
Part of book
Fecha de publicación:
2017
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/11616
Acceso en línea:
http://hdl.handle.net/10614/11616
https://link.springer.com/chapter/10.1007/978-3-319-59773-7_51
https://link.springer.com/content/pdf/10.1007%2F978-3-319-59773-7.pdf
https://doi.org/10.1007/978-3-319-59740-9_23
Palabra clave:
Center kernel alignment
Feature selection
Feature selection
Human motion
Kinematics
Motion capture data
Principal component analysis
Relevance
Machining
Milling (metal-work)
Bayesian statistical decision theory
Mecanizado
Fresado (metalistería)
Teoría bayesiana de decisiones estadísticas
Manufacturing processes
High-speed machining
Micromachining
Mecanizado de alta velocidad
Procesos de manufactura
Corte de metales
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
Description
Summary: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