Titanium has a wide variety of applications, particularly in the aerospace industry. However, because of its low thermal conductivity and high strength, machining of titanium is very difficult. The heat generated in machining can dramatically shorten the tool life. Rotary ultrasonic machining (RUM) is a non-traditional machining process, and has been used to machine various difficult-to-machine...

Rotary ultrasonic machining (RUM) is a relatively low cost and environmentally benign process for machining of advanced ceramics. Much effort has been made to theoretically and experimentally investigate material removal rate, surface roughness, and tool wear in RUM. However, there is no report on systematic study of edge chipping in RUM drilling of ceramics. This paper presents the results of ...

This study characterized the relationship between ruminal temperature (T rum) and pH in beef cattle fed growing and finishing diets. In Exp. 1, 16 ruminally cannulated beef heifers (388.5 ± 34.9 kg BW) were fed 4 growing diets in a replicated 4 × 4 Latin square with four 21-d periods. Diets were (DM basis) grower control (CTLg; 35% barley grain plus 5% canola meal), 40% corn dried distillers gr...

Artificial aging techniques were applied to samples of rum. These samples were then compared, by artificial neural nets, with traditionally aged rums. Analysis was based on the phenolic and furanic composition of each sample. There were found to be few statistical differences between samples, thus confirming the possibility of applying artificial aging techniques to obtain rum with phenolic and...

This paper establishes that every random utility discrete choice model (RUM) has a representation that can be characterized by a choice-probability generating function (CPGF) with speci c properties, and that every function with these speci c properties is consistent with a RUM. The choice probabilities from the RUM are obtained from the gradient of the CPGF. Mixtures of RUM are characterized b...

The relevance vector machine(RVM) is a state-of-the-art constructing sparse regression kernel model [1,2,3,4]. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). In RVM and SVM, relevance vectors (RVs) and support vectors (SVs) are both selected from the input vector set. This may limit model flexibility. In ...