Intelligent maintenance prediction system for LED wafer testing machine

Achieving high quality production of light-emitting diode (LED) wafers requires robust monitoring and the use of a stable test machine. In many factories, production continues 24 h a day. Stopping the manufacturing process at a factory is often difficult. Therefore, reducing inspection time and ensuring the stability of test machines are important. Traditionally, LED wafer factories examine their test machines during periodic maintenance. Standard lamp adjustments are performed to ensure their accuracy. This process interrupts the manufacturing process and requires extra manpower. It reduces productivity and increases production cost. Additionally, the accurate assessment of the aging of the components of the machine requires an experienced engineer. Correctly timing the maintenance and replacing the aging components of the LED wafer test machine are important. This work performed feature extraction to identify the working attributes of an LED wafer test machine. The intelligent maintenance prediction system then uses the radial basis function neural network and variability of the working attributes to predict the maintenance times and aging of the LED wafer test machines. Experimental results reveal that the accuracy of proposed system in predicting maintenance times exceeds 98 %.     

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process.     

Image Retrieval With Relevance Feedback Based on Graph-Theoretic Region Correspondence Estimation

This paper presents a graph-theoretic approach for interactive region-based image retrieval. When dealing with image matching problems, we use graphs to represent images, transform the region correspondence estimation problem into an inexact graph matching problem, and propose an optimization technique to derive the solution. We then define the image distance in terms of the estimated region correspondence. In the relevance feedback steps, with the estimated region correspondence, we propose to use a maximum likelihood method to re-estimate the ideal query and the image distance measurement. Experimental results show that the proposed graph-theoretic image matching criterion outperforms the other methods incorporating no spatially adjacent relationship within images. Furthermore, our maximum likelihood method combined with the estimated region correspondence improves the retrieval performance in feedback steps.     

Flow patterns in gas-assisted injection molding process in a channel

The flow field of a long bubble steadily expelling a viscous fluid confined by two closely located parallel plates is examined. In order to investigate the influence of bubble size on the flow field, a theoretical bubble profile is used to replace the complicated procedure for computing simultaneously the interface between the gas surface and fluid flows. The present study showed the two typical flow patterns and also a third flow pattern of the stagnation point moving in the region of the bubble tip front during transformation of the two typical flow patterns. The vorticity patterns are also drawn for various bubble profiles and are examined for their effect on the flow. The velocity field is also presented from two different viewpoints and the phenomena is examined. The stagnation point located on the center line between the bubble tip to the upstream is only found in the small range of in a channel, where λ is ratio of the bubble width to the distance between two parallel plates.     

Mechanical behaviour of cadmium-boron and cadmium-tungsten particulate composites

Mechanical behaviour of particulate composites of cadmium containing 0.6 to 3 m size particles of boron and tungsten (up to 30 vol %) were studied from –196 to 260° C (0.13 to 0.9T _m). The marked strengthening of cadmium by the presence of fine particles is attributed to significant grain size and texture strengthening effects as well as to dispersion hardening effects.