In the design of cylindrical roller bearings (CRBs), a long life is one of the most essential criteria. However, the life of bearings depends on the multitude of factors that includes the fatigue, lubrication, and thermal characteristics in bearings. In the present work, three primary objectives namely, the dynamic capacity, the elastohydrodynamic lubrication (EHL) minimum film-thickness, and the maximum temperature have been optimized, sequentially. Some of these objectives may be contradicting to each other. The optimum bearing design has been attempted by first deriving a constrained nonlinear formulation and then optimizing it with an evolutionary algorithm. Constraint violations study has been performed to have assessment of the effectiveness of each of the constraints. A convergence study has been carried out to ensure the near global optimum point in the design. In terms of the basic dynamic capacity of the bearing, there is an excellent conformity among the optimized and customary bearings. A sensitivity study of various geometric design variables has been performed to see changes in objective functions and results show that geometric variables have hardly any undesirable influence.

In high-speed applications the maximum temperature in bearings are a crucial concern. In some applications the bearing is the prime source of heat, the temperature at which a bearing operates dictates the type and amount of lubricant and the material for the fabrication of the bearing components. In the present work a thermal based optimum design of tapered roller bearings has been presented. Internal geometry of the bearing has been optimized based by evolutionary algorithm. Constraints are geometrical, kinematical, strength and thermal in nature. Optimum designs have been found to have better performance parameters. Artificial bee colony algorithm has been used for the present optimization problem, for solving constrained non-linear optimization formulations. A total of nine design variables corresponding to the bearing geometry and constraint factors have been considered. A convergence study has been carried and optimum designs based on temperature is compared with the optimized values based on dynamic capacity, both using artificial bee colony algorithm. There is an excellent improvement found in the optimized bearing designs based on temperature when compared with the optimized results based on dynamic capacity in respect of the maximum temperature in the bearing with the artificial bee colony algorithm.

BackgroundIn the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles.ResultsThe Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit.ConclusionsThe Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.

Purpose In optimum designs of deep-groove ball bearings (DDGBs), an extended service life is one of the vital criteria. The life of a bearing depends on several factors. The purpose of this paper is to sequentially optimize three prime objectives for DDGB, i.e. the dynamic capacity (Cd), the maximum bearing temperature (Tmax) and the elasto-hydrodynamic minimum film thickness (Hmin). Design/methodology/approach For solving constrained non-linear optimization formulations with multitude of objectives, an optimal design methodology has been put forth with the help of artificial bee colony algorithms. A study on the constraint violation has been carried out. By the Monte Carlo simulation method, a sensitivity investigation of diverse design variables has been done to examine variations in three objective functions and violation of constraints. Findings Excellent improvement in the dynamic capacity (Cd), the maximum bearing temperature (Tmax) and the elasto-hydrodynamic minimum film thickness (Hmin) have been found in optimized bearing designs. Originality/value Ball bearing design has been done based on multi-discipline objectives that are based on strength, tribology and thermal consideration. This type of design is essential in practical scenario where these physical phenomena will be present simultaneously.