Numerical Simulation of Temperature Field for Laser Rapid Prototyping
The temperature field model for laser rapid prototyping is established by the mathematical treatment of laser power input. Temperature fields with different process conditions are numerical simulated, and the temperature is Gaussian distribution as the intensity distribution on laser beam cross-section. Temperature fields for laser rapid prototyping ware measured by thermal imager in real time. The simulation results agree with the experimental measurement. Experiments show that the temperature field model can be used for computer analysis and optimizing of laser rapid prototyping processing.
Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making
Rapid prototyping (RP) is an emerging technology for creating physical prototypes from three-dimensional computer aided design models using the additive process with layers. In order to use the process efficiently, several important factors must be considered. The ability to select the optimal orientation of build-up is one of the critical factors since it affects the part quality, build time, and part cost. This study aims to determine the optimal build-up direction of a part for different RP systems. The revised average weighted surface roughness, which considers stair stepping effect, build time calculated by laser/knife/nozzle travel, and part cost calculated by build cost rate, labor cost rate, material cost, etc., are considered. Among the orientation candidates chosen from the convex hull of a model, the best orientation is selected using the simple additive weighting method, a widely used multi-criterion method for decision making. The best orientation is also identified for each criterion. The validity of the algorithm is demonstrated by several examples. The algorithm can help RP users select the best build-up direction of the part and create optimal process planning.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.