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http://comimsa.repositorioinstitucional.mx/jspui/handle/1022/399| Optimization by Estimation of Distribution Algorithms in the Machining of Titanium (Ti 6Al 4V) Alloy using Predicting Surface Roughness using Neural Network Modeling | |
| Pedro Pérez Villanueva | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| OPTIMIZATION AND ESTIMATION | |
| Though the initial applications of titanium alloys have been in aerospace industries such as aero-engine and airframe manufacturing, there is a growing trend in their application in the industrial sector, including petroleum refining, chemical and food processing, surgical implantation, nuclear waste storage, and other automotive and marine applications. One of the more popular titanium alloys for these applications is Ti–6Al–4V, which comprises about 45–60% of the total titanium products in practical use [Mantle et al., 1998; Ezugwu et al., 1997]. Despite the increased usage and production of titanium and its alloys, these materials fall under the category of materials those are the most difficult to machine materials. In the machining of parts, surface quality is one of the most specified customer requirements. A major indication of surface quality on machined parts is surface roughness. Surface roughness is the result of process parameters such as tool geometry and cutting conditions (feed rate, cutting speed, depth of cut, and others). The quality and the integrity of the finish-machined surfaces are affected by the workpiece material hardness and other properties. In quantifying surface roughness, the average surface roughness definition, which is often represented by the Ra symbol, is commonly used. Theoretically, Ra is the arithmetic average value of the departure of the profile from the mean line throughout the sampling length [Sander, 1991]. Ra is also an important factor in controlling machining performance. | |
| 2010 | |
| Capítulo de libro | |
| Inglés | |
| Estudiantes Investigadores | |
| Indira Gary Escamilla Salazar, Pedro Perez Villanueva, “Optimization by Estimation of Distribution Algorithms in the Machining of Titanium (Ti 6Al 4V) Alloy using Predicting Surface Roughness using Neural Network Modeling,” in Optimization by Estimation of Distribution Algorithms in the Machining of Titanium Alloy, 1 st ed., pp. 233-245. | |
| INGENIERÍA Y TECNOLOGÍA | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | ARTÍCULOS/ CAPÍTULOS DE LIBRO / MEMORIAS DE CONGRESO |
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| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| 2010-C.L.-Optimization by Estimation of Distribution-INDIRA.pdf | Indira Gary Escamilla Salazar, Pedro Perez Villanueva, “Optimization by Estimation of Distribution Algorithms in the Machining of Titanium (Ti 6Al 4V) Alloy using Predicting Surface Roughness using Neural Network Modeling,” in Optimization by Estimation of Distribution Algorithms in the Machining of Titanium Alloy, 1 st ed., pp. 233-245. | 1.06 MB | Adobe PDF | Visualizar/Abrir |