Aplicación de Redes Neuronales en la Detección de Regímenes Degradados en el Proceso Wedm
[EN] This article presents the results of a comparative study performed to select the most appropriate neural network configuration for Wire Electrical Discharge Machining (WEDM). The main objective is to detect instability trends that allow alerting to the increasing risk of wire breakage of the cu...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/145374 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/145374 |
| Access Level: | acceso abierto |
| Palabra clave: | WEDM Electroerosión RNA Redes neuronales artificiales Perceptrón Multicapa Elman Electro-discharge machining ANN Artificial Neural Network Perceptron Multilayer |
| Sumario: | [EN] This article presents the results of a comparative study performed to select the most appropriate neural network configuration for Wire Electrical Discharge Machining (WEDM). The main objective is to detect instability trends that allow alerting to the increasing risk of wire breakage of the cutting tool: the wire. The wire breakage reduces the process productivity and the required accuracy. Considering the results of previous works of the authors, in which different types of degraded behaviors were identified, a comparative study that considers different aspects has been performed. Among them, the evaluation of classic neural architectures stands out, in particular, the static architecture MultiLayer Perceptron (MLP), and the recurrent architecture Elman. The main conclusion of this work is that the Elman architecture is the most approppriate for detecting the degradation of the cutting process. |
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