Geometric Transformation Method Research Articles

Alternative techniques for casting process simulation

Over the past 20 years, casting process simulation has been an active area of research. The simulation techniques are either based on solving governing partial differential equations using numerical schemes such as the finite element or finite difference methods, or a variety of heuristically based geometry driven methods. Numerical methods are more accurate, but geometry driven methods are computationally less expensive. This paper explores two alternative techniques to overcome some of the limitations of traditional numerical simulation schemes for the casting process simulation. The first technique uses a geometric transformation method known as the medial axis transformation, to predict hot spots whereas the second technique, based on meshless methods, is used for simulating the mould filling process.

Computation of 3-D open boundary eddy-current fields in large transformers using T, ψ-φ/sub m/ method coupled with GTM

Three-dimensional distributions of eddy-current densities and loss densities in metals near heavy current leads in a large transformer are calculated by using the T, /spl psi/-/spl phi//sub m/ method coupled with the geometric transformation method. The method is verified through TEAM Problem 21. The local overheating regions can be found out according to the point where the maximum of the loss density is. The loss densities in the ascending flange are analyzed at the different materials and thicknesses of copper screen.

Robot location determination in a complex environment by multiple marks

A new approach of using multiple marks for determining three-dimensional robot location in a complex environment, which may be a composed space or a space with rough terrain, is proposed. One principal idea is to pre-locate a large enough number of marks with different labels at different planar places that at least one mark can be seen by the camera, wherever the robot is in the working space. The other concept is that by integrating the pattern recognition technique with the three-dimensional geometrical transformation method, the system can determine the robot location without the need of fitting any prerequisite alignment. This novel property enables the robot to work in a space with rough terrain. Experimental results from using three marks in a space composed of two warehouses have shown that the location error is less than 3% on average.