Fiber laser processing of amorphous rare earth NeFeB magnetic materials

Since the exchange coupling theory was proposed by Kneller and Hawig in 1991 there has been a significant effort within the magnetic materials community to enhance the performance of rare earth magnets by utilising nano-composite meta-materials. Inclusions of magnetically soft iron smaller than approximately 10 nm in diameter are exchange coupled to a surrounding magnetically hard Nd2Fe14B matrix and provide an enhanced saturisation magnetisation without reducing coercivity. For such a fine nanostructure to be produced, close control over the thermal history of the material is needed. A processing route which provides this is laser annealing from an amorphous alloy precursor. In the current work, relationships between laser parameters, thermal histories of laser processed amorphous stoichiometric NdFeB ribbons and the magnetic properties of the resulting nanocrystalline products have been determined with a view to applying the process to thick film nanocomposite magnet production.

Performance of thin film carbon materials and carbon nanotubes as cold cathodes

The field emissions from three different types of carbon films are studied using a Kiethly voltage-current source-measure unit under computer control. The three types of carbon films are : 1) a-C:H:N deposited using an inductively coupled rf PECVD process, where the N content in the films can be as high as 30 at %; 2) cathodic arc deposited tetrahedral amorphous carbon with embedded regions of carbon nanotube and anion structures and 3) unoriented carbon nanotube films on a porous substrate. The films are formed by filtering a solution of nanotubes dispersed in alcohol through the pores and drying.

Optimal design of Functionally Graded Materials using a procedural model and Particle Swarm Optimization

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples. © 2011 Elsevier Ltd. All rights reserved.