SURFACE ROUGHNESS PREDICTED MODELING IN MACHINING OF TI 6AL 4V ALLOY USING NEURAL NETWORK AND LINEAR REGRESSION
Titanium alloys are attractive materials due to their unique high strength, excellent performance at elevated temperatures and exceptional corrosion resistance. The aerospace and military industries are the main users of this material. Titanium alloys are classified as difficult machining materials....
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | México |
| Institución: | Corporación Mexicana de Investigación en Materiales |
| Repositorio: | Repositorio COMIMSA |
| Idioma: | inglés |
| OAI Identifier: | oai:comimsa.repositorioinstitucional.mx:1022/394 |
| Acceso en línea: | http://comimsa.repositorioinstitucional.mx/jspui/handle/1022/394 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/ARTÍCULO/NEURAL NETWORK info:eu-repo/classification/cti/7 |
| Sumario: | Titanium alloys are attractive materials due to their unique high strength, excellent performance at elevated temperatures and exceptional corrosion resistance. The aerospace and military industries are the main users of this material. Titanium alloys are classified as difficult machining materials. The correct parameters of machining are a hard setting, actually researches are looking to develop new models to predict and optimize these parameters. The surface roughness (Ra) in turning of a titanium alloy machining Ti 6Al 4V was predicted using neural network and linear regression is shown. The machining tests were carried out using PVD (TiAIN) coated carbide inserts under different cutting conditions. Confidence intervals were estimated in the model to get correct results. There are various machining parameters and they have an effect on the surface roughness. A set of initial parameters in finished turning of Ti 6Al 4V obtained from literature have been used. These parameters are cutting speed, feed rate and depth of cut. The results showed the advantages of use a Neural –Statistical approach to analyze the variables and to model the machining process. |
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