University of Extremadura

About: University of Extremadura is a education organization based out in Badajoz, Spain. It is known for research contribution in the topics: Population & Hyperspectral imaging. The organization has 7856 authors who have published 18299 publications receiving 396126 citations. The organization is also known as: Universidad de Extremadura.

In-process surface roughness prediction system using cutting vibrations in turning

Abstract: Machining is a complex process in which many variables can affect the desired results. Among them, surface roughness is a widely used index of a machined product quality and, in most cases, is a technical requirement for mechanical products since, together with dimensional precision, it affects the functional behavior of the parts during their useful life, especially when they have to be in contact with other materials. In-process surface roughness prediction is, thus, extremely important. In this work, an in-process surface roughness estimation procedure, based on least-squares support vector machines, is proposed for turning processes. The cutting conditions (feed rate, cutting speed, and depth of cut), parameters of tool geometry (nose radius and nose angle), and features extracted from the vibration signals constitute the input information to the system. Experimental results show that the proposed system can predict surface roughness with high accuracy in a fast and reliable way.

Recommended Correlations for the Surface Tension of Common Fluids

Abstract: Available values of the surface tension of 81 common fluids were fitted by using the same model presently used in the REFPROP program V9.0 by NIST. A set of data was built for every fluid by including mainly those values given in the DIPPR and DETHERM databases. For some fluids, other available sources of data were added in order to obtain adequate sets. For every fluid, we checked the accuracy of the REFPROP program and we made a new fit in order to improve the performance of the correlation. We found good general agreement between the REFPROP predictions and the data for 44 fluids, and therefore only very slight improvements are made for them. For the other 37 fluids, the REFPROP correlation can be more clearly improved. In particular, our new correlation is significantly more accurate for ammonia, deuterium, ethanol, and neon, because the version of REFPROP used gives values in clear disagreement with other data sources.