Introducing minimum energy tool path in 5-axis flank milling

International audienceThis paper deals with a surface based-approach for 5-axis flank milling of complex surfaces. The surface representation of the tool trajectory allows globally minimizing the geometrical deviations between the machined surface and the designed surface. However, within the context of high-speed machining, the smoothness of the calculated tool trajectory is essential to ensure high performance machining. Indeed, oscillatory trajectories may penalize process efficiency. Taking advantages of the surface based approach, the smoothness of the trajectory can be controlled through the estimation of the energy of deformation of the tool path surface. Minimum energy tool paths lead to minimal machining time. As geometrical deviations are not minimised for minimum energy tool paths, a compromise must be done to find the best solution

Geometrical deviations versus smoothness in 5-axis high-speed flank milling

International audienceThe paper deals with the Generation of Optimized 5-aXis Flank milling trajectories. Within the context of 5-axis High-Speed Machining, oscillatory trajectories may penalize process efficiency. The control of the trajectory smoothness is as essential as the control of geometrical deviations. For this purpose the Geo5XF method based on the surface representation of the tool trajectory has been developed. In flank milling, this surface, also called the Machining Surface (MS), is the ruled surface locus of the tool axes defining the trajectory. Based on a first positioning, the method aims at globally minimizing geometrical deviations between the envelope surface of the tool movement and the designed surface by deforming the MS while preserving trajectory smoothness. The energy of deformation of the MS is used as an indicator of the smoothness. Hence, in most cases, results obtained using Geo5XF show that minimum energy tool paths lead to minimal machining time. As geometrical deviations are not minimized for minimum energy tool paths, a compromise must be reached to find the best solution

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data