Artificial Neural Network Based on Genetic Learning for Machining of Composite Materials
In this paper an Artificial Neural Network (ANN) aiming the efficient modeling of a set ofmachining conditions in the orthogonal cutting of composite materials is presented. Theexperimental procedure considers process parameters as cutting speed and feed rate, the typeof insert of the tool and the t...
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| Outros Autores: | , |
| Formato: | book |
| Idioma: | eng |
| Publicado em: |
2006
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| Assuntos: | |
| Texto completo: | https://repositorio-aberto.up.pt/handle/10216/99492 |
| País: | Portugal |
| Oai: | oai:repositorio-aberto.up.pt:10216/99492 |
| Resumo: | In this paper an Artificial Neural Network (ANN) aiming the efficient modeling of a set ofmachining conditions in the orthogonal cutting of composite materials is presented. Theexperimental procedure considers process parameters as cutting speed and feed rate, the typeof insert of the tool and the type of workpiece material in order to obtain a set of results usedfor ANN learning. The supervised learning of the ANN is based on a genetic algorithm withan elitist strategy. Input, hidden and output layers model the topology of the ANN. Theweights of the synapses, the bias for the hidden and output nodes and the number of neuralnodes of the hidden layer are used as design variables. Sigmoid activation functions are usedin hidden and output layers. The square error between experimental and numerical results isused to monitoring the learning process aiming to obtain the completeness of modeling of themachining process. |
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