Sang-Hyun Cho

A fast algorithm for voxel-based deterministic simulation of X-ray imaging ☆

Abstract Deterministic method based on ray tracing technique is known as a powerful alternative to the Monte Carlo approach for virtual X-ray imaging. The algorithm speed is a critical issue in the perspective of simulating hundreds of images, notably to simulate tomographic acquisition or even more, to simulate X-ray radiographic video recordings. We present an algorithm for voxel-based deterministic simulation of X-ray imaging using voxel-driven forward and backward perspective projection operations and minimum bounding rectangles (MBRs). The algorithm is fast, easy to implement, and creates high-quality simulated radiographs. As a result, simulated radiographs can typically be obtained in split seconds with a simple personal computer. Program summary Program title: X-ray Catalogue identifier: AEAD_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEAD_v1_0.html Program obtainable from: CPC Program Library, Queens University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 416 257 No. of bytes in distributed program, including test data, etc.: 6 018 263 Distribution format: tar.gz Programming language: C (Visual C++) Computer: Any PC. Tested on DELL Precision 380 based on a Pentium D 3.20 GHz processor with 3.50 GB of RAM Operating system: Windows XP Classification: 14, 21.1 Nature of problem: Radiographic simulation of voxelized objects based on ray tracing technique. Solution method: The core of the simulation is a fast routine for the calculation of ray-box intersections and minimum bounding rectangles, together with voxel-driven forward and backward perspective projection operations. Restrictions: Memory constraints. There are three programs in all. • A. Program for test 3.1(1): Object and detector have axis-aligned orientation; • B. Program for test 3.1(2): Object in arbitrary orientation; • C. Program for test 3.2: Simulation of X-ray video recordings. 1. Program A Memory required to execute with typical data: 207 Megabytes, depending on the size of the input file. Typical running time: 2.30 s. (Tested in release mode, the same below.) 2. Program B (the main program) Memory required to execute with typical data: 114 Megabytes, depending on the size of the input file. Typical running time: 1.60 s. 3. Program C Memory required to execute with typical data: 215 Megabytes, depending on the size of the input file. Typical computation time: 27.26 s for cast-5, 101.87 s for cast-6.

Basic construction of intelligent expert system for riser design using database system and optimisation tools

Abstract Process design using computer simulation and optimisation has recently garnered a great deal of attention and much progress has been made through research in this area. Most research addresses the soundness of mass products, productivity optimisation, and improvement of product reliability. In the casting industry, a mainstay of the manufacturing sector, the design method of an optimal riser has also advanced greatly and it requires a clearly defined optimisation strategy. The purpose of working out a proper optimisation strategy is to minimise the time required for long series and iterations of calculations and thereby achieve an effective result. To this end, a database system was built by means of an image retrieval method. The authors also studied a way to automatically designate an appropriate point for installing a riser using solidification simulation and the Method of Modified Feasible Direction (MMFD), an optimisation method that uses the gradient search. The present study outlines the basic algorithm for the Intelligent Expert System (IES) for design of an optimal riser. It employs solidification simulation, a database system, and the MMFD. The study also addresses the application of the system to a simple cast.

Development of Simulation Service Platform Based on Cloud Computing for Manufacturing Industry

Computer Aided Engineering (CAE) is very helpful field for every manufacturing industry including foundry. It covers CAD, CAM, and simulation technology also, and becomes as common sense in developing new products and processes. In South Korea, more than 600 foundries exist, and their average employee number is less than 40. Moreover, average age of them becomes higher. To break out these situations of foundry, software tools can be effective, and many commercial software tools had already been introduced. But their high costs and risks of investment act as difficulties in introducing the software tools to SMEs (Small and Medium size Enterprise). So we had developed cloud computing platform to propagate the CAE technologies to foundries. It includes HPC (High Performance Computing), platforms and software. So that users can try, enjoy, and utilize CAE software at cyber space without any investment. In addition, we also developed platform APIs (Application Programming Interface) to import not only our own CAE codes but also 3rd-party’s packages to our cloud-computing platforms. As a result, CAE developers can upload their products on cloud platforms and distribute them through internet.

The contribution of the nucleation process to grain formation in calculating solidification microstructure by CA–DFD

Abstract To predict solidification macrostructures, the direct finite difference (DFD) method for heat transfer calculation was coupled with cellular automaton (CA). In the CA-DFD, the nucleation process as the initial condition of calculations was determined according to Gaussian distribution or being instantaneous. In the case of Gaussian distribution, there are six nucleation-related parameters. In this work, the inspection of relationships of preset nuclei to grain, preset nuclei to born nuclei, and born nuclei to grain was carried out by comparison of each ratio. However, these relationships are only valid under the same solidification environments. In contrast to the preset method, the successive nuclei setting method (SNS) was carried out. This method manages the nucleus settings by every time step. The microstructures by SNS were compared with those by preset.

CAE Services on Cloud Computing Platform in South Korea

We, KITECH, had developed CAE software for casting industries(foundries) over last 10 years, and distributed the developed CAE package for foundries in south korea. But the gorwth rate of market for CAE technologies becomes slower because of expensive prices of CAE tools and lacks of trials for introducton of new technologies into their manufacturing workplaces. So that we have been developing new concept market for CAE tehcnologies for last 5 years, and built cloud platform services for CAE in south korea, and it is ISC(Internet Simulation Center). We setup cluster computing systems, datacenter for infrastructure, and developed various services and relational interfaces to serve cloud computing services. So that we have loaded 4 CAE contents into our cloud computing platform, and are providing CAE services to domestic users.

The present and future application of computer simulation for casting design in Korean foundries

This paper shows applications and trials used to utilize computer-aided simulations for enhancing the productivity and quality of casting products in Korean foundries initially over a few decades. In Korea, KITECH (Korea Institute of Industrial Technology) took the important steps in developing and propagating computer simulation software for casting design. The “Z-CAST” developed by KITECH is the best computer simulation software for casting design in Korea and its schematics and application for the manufacturing field are introduced in this paper. Also, the future work of the casting industry is stated, especially, IES (Intelligent Expert System), the main stream of computer-aided production in Korea, is discussed.

Immersive Visualization of Casting Flow and Solidification

We present an immersive visualization system for visual analysis of casting flow and solidification using a VR display. Metal flow and solidification are simulated using finite-difference (FDM) software. From the simulation results we extract the surface of the casting on a rectangular mesh and give each facet a color corresponding to the temperature at that position. We then transfer this color data to the original STL model of the casting. The surface of the STL model is first subdivided. Then, for each point on this fine mesh, the nearest point on the grid surface model is determined, and the color is transferred to the STL model. A Projection Table is used to display stereoscopic images of the model to provide an effective visual analysis environment.