Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process
Año:2009
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Machine tool automation is an important aspect for manufacturing companies facing the growing demand of profitability and high quality products as a key for competitiveness. The purpose of supervising machining processes is to detect interferences that would have a negative effect on the process but mainly on the product quality and production time. In a manufacturing environment, the prediction of surface roughness is of significant importance to achieve this objective. This paper shows the efficacy of two different machine learning classification methods, Bayesian networks and artificial neural networks, for predicting surface roughness in high-speed machining. Experimental tests are conducted using the same data set collected in our own milling process for each classifier. Various measures of merit of the models and statistical tests demonstrate the superiority of Bayesian networks in this field. Bayesian networks are also easier to interpret that artificial neural networks.
Internacional
Si
JCR del ISI
Si
Título de la revista
EXPERT SYSTEMS WITH APPLICATIONS
ISSN
0957-4174
Factor de impacto JCR
2,596
Información de impacto
Volumen
36
DOI
Número de revista
3
Desde la página
7270
Hasta la página
7279
Mes
ENERO
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Participante: M. Correa (Spanish Natl Res Council)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Participante: J. Pamies-Teixeira (Univ. Nova Lisboa)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Inteligencia Artificial
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