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

• Type: Article in International Scientific Journal
• Date: 2008

Authors

C. A. Conceição António (Author) FEUP; J. Paulo Davim (Author) Other; Vítor Lapa (Author) Other

Journal

Title: International Journal of Advanced Manufacturing Technology

Vol. 39 No. 11-12

Pages: 1101-1110

ISSN: 0268-3768

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 14 Citations

Scopus - 19 Citations

INSPEC

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10