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LINEAR REGRESSION MODELING IN MACHINING OF TI 6AL 4V ALLOY USING TO PREDICT SURFACE ROUGHNESS
Pedro Pérez Villanueva
Acceso Abierto
Atribución-NoComercial-SinDerivadas
LINEAR REGRESSION
The objective of this work is to present a methodology to predict the roughness during the machining process of the Ti 6Al 4V, with a linear regression model, considering the parameters of speed rate, feed and depth as an input. The data used in this job was taken from work conduce in FIME, using milling process in a rectangular pieces of titanium (Ti 6Al-4V), the tool was an endmill coated with Aluminum Titanium Nitride (AlTiN) with 4 cutting edge and 3/8" on a diameter of the tool. The milling was carried out over a length of 47 mm, using a design of experiment with 3 factors and 3 levels, giving a total of 27 experiments with 3 different tests. The roughness was measure with a ZEISS Perfilometers Surfcom 1500 SD2 with an automatic control. The model is useful to build a surface of the response of the machining process. This model can be used to predict the effect in roughness when the parameters are changed without risks and high costs; however, the model must be validated in order to be used as a predictor. The results indicate the ways to get a good model.
2009-10
Artículo
LINEAR REGRESSION MODELING IN MACHINING OF TI 6AL 4V ALLOY USING TO PREDICT SURFACE ROUGHNESS I. Escamilla, L. Torres, P. Perez, P. Zambrano, and B. Gonzalez. Proceedings of the 14th Annual International Conference on Industrial Engineering Theor
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CIENCIAS TECNOLÓGICAS
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publishedVersion - Versión publicada
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2009-LINEAR REGRESSION.pdfLINEAR REGRESSION MODELING IN MACHINING OF TI 6AL 4V ALLOY USING TO PREDICT SURFACE ROUGHNESS I. Escamilla, L. Torres, P. Perez, P. Zambrano, and B. Gonzalez. Proceedings of the 14th Annual International Conference on Industrial Engineering Theor334.88 kBAdobe PDFVisualizar/Abrir