Selection of a neural network for modelling the honing process
Document typeConference report
Rights accessRestricted access - author's decision
Roughness obtained in honing process depends on many different process parameters, such as grain size of abrasive stones, pressure of stones on the workpiece’s surface, density of abrasive, tangential speed of the honing head and linear speed of the honing head. This fact makes it difficult to study the process from an analytical point of view. For this reason, use of empirical methods or use of artificial intelligence is recommended in this case. In the present paper, results about use of neural networks for obtaining average roughness Ra as a function of honing parameters are presented. Best neural network was chosen among different possibilities. For doing this, experimental results were divided into three groups: 70 % of results were used for training, 15 % of results were used for validation and 15 % of results were used as test to compare networks with other models. The best neural network was considered to be the one with lowest errors using the validation experimental results.
CitationSivatte, M. [et al.]. Selection of a neural network for modelling the honing process. A: International Reseach/Expert Conference. "Proceedings of 17th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology” TMT 2013". Istanbul: 2013, p. 1-4.
- GREC - Grup de Recerca en Enginyeria del Coneixement - Ponències/Comunicacions de congressos 
- TECNOFAB - Grup de Recerca en Tecnologies de Fabricació - Ponències/Comunicacions de congressos 
- Departament d'Enginyeria mecànica - Ponències/Comunicacions de congressos 
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos