Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials

• Tipo: Artigo em Revista Científica Internacional
• Data: 2008

Autores

C. A. Conceição António (Autor) FEUP; J. Paulo Davim (Autor) Outra; Vítor Lapa (Autor) Outra

Revista

Título: International Journal of Advanced Manufacturing Technology

Vol. 39 Nº 11-12

Páginas: 1101-1110

ISSN: 0268-3768

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 14 Citações

Scopus - 19 Citações

INSPEC

Outras Informações

ID Authenticus: P-003-TGF

DOI: 10.1007/s00170-007-1304-5

Abstract (EN): In this paper an artificial neural network (ANN) aiming for the efficient modelling of a set of machining conditions for orthogonal cutting of polyetheretherketone (PEEK) composite materials is presented. The supervised learning of the ANN is based on a genetic algorithm (GA) supported by an elitist strategy. Input, hidden and output layers model the topology of the ANN. The weights of the synapses and the biases for hidden and output nodes are used as design variables in the ANN learning process. Considering a set of experimental data, the mean relative error between experimental and numerical results is used to monitor the learning process obtaining the completeness of the machining process modelling. Also a regularization term associated to biases in hidden and output neurons are included in the GA fitness function for learning. Using a different set of experimental results, the optimal ANN obtained after learning is tested. The optimal number of nodes on the hidden layer is searched and the positive influence of the regularization term is demonstrated. This approach of ANN learning based on GA presents low mean relative errors in learning and testing phases.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10