Material Characterization of Photo-Fabrication Process

The characterization of photopolymerization for the rapid prototyping process has been studied using a laser stereolithography machine. The cross sections of the cured layers and lines were examined using an optical microscope. Perkin-Elmer differential scanning calorimeter (DSC)-7 was employed to investigate the degree of curing after laser scanning. It was observed that the profile of the cross section of a cured line and/or part determined by the laser exposure density is a function of laser power and scanning speed. DSC analysis shows the retained resin to be governed by laser exposure intensity and laser scanning pattern. By increasing the laser exposure or the overlapping area of two adjacent scanning lines, retained and uncured resin could be minimized. However, the efficiency of photopolymerization is lowered. The most efficient operating parameters for part building can be determined by a T factor.     

Micro-pillars enhanced all vanadium redox flow batteries and the effect of assembly torque on the electrochemical performance

A uniformly distributed electrolyte system based on enhanced micro-pillars flow field design is proposed for a vanadium redox flow battery (VRFB). The uniformity experiments show that the flow uniformity can be effectively increased and normalized velocity variation for any channel is measured to be less than 18 %, demonstrating the effectiveness of micro-pillars for enhanced flow field. Experimental validation is carried out experimentally and the effect of different assembly torque is also investigated for VRB cell electrochemical performance via polarization and cyclic voltammograms (CV) test. The results show that an optimal assembly torque (11 N-m) can yield a significant improvement of 68.5 % current density when compared with the case of 10 N-m. Furthermore, the CV results clearly indicate that the optimal assembly torque with enhanced micro-pillars design can result in the 37.3 % improvement of electrochemical surface area. Moreover, the contact pressure contours from the pressure-sensitive films are measured to check the influence on the configuration of VRFB unit cell.

     

Automated process parameter resetting for injection moulding: a fuzzy-neuro approach

Optimal process parameter setting for injection moulding is difficult to achieve due to a large number of factors involved. Current practice in industry is to adjust the parameters based on the products' defects of test-run through trial and error. This process, however, requires enormous experience and is often time consuming. This paper reports an intelligent system employing fuzzy sets and neural networks which is able to predict the process parameter resetting automatically to achieve better product quality. The system is designed to be used in the test-run of injection moulding. Seven commonly encountered injection moulded product defects (short shot, flash, sink-mark, flow-mark, weld line, cracking, and warpage) and two key injection mould parameters (part flow length and flow thickness) are used as system input which are described using fuzzy terms. On the other hand, nine process parameter adjusters (pressure, speed, resin temperature, clamping force, holding time, mould temperature, injection holding pressure, back pressure, and cooling time) are the system output. A back-propagation neural network has been constructed and trained using a large number of {defects} {parameter adjusters} expert rules. The system is able to predict the exact amount to be adjusted for each parameter towards reducing or eliminating the observed defects. Testing in several real cases showed that the system produced satisfying results.     

Automatic change detection of driving environments in a vision-based driver assistance system

Detecting critical changes of environments while driving is an important task in driver assistance systems. In this paper, a computational model motivated by human cognitive processing and selective attention is proposed for this purpose. The computational model consists of three major components, referred to as the sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts temporal and spatial information from video sequences. The extracted information serves as the input stimuli to a spatiotemporal attention (STA) neural network embedded in the perceptual analyzer. If consistent stimuli repeatedly innervate the neural network, a focus of attention will be established in the network. The attention pattern associated with the focus, together with the location and direction of motion of the pattern, form what we call a categorical feature. Based on this feature, the class of the attention pattern and, in turn, the change in driving environment corresponding to the class are determined using a configurable adaptive resonance theory (CART) neural network, which is placed in the conceptual analyzer. Various changes in driving environment, both in daytime and at night, have been tested. The experimental results demonstrated the feasibilities of both the proposed computational model and the change detection system.     

Road-sign detection and tracking

In a visual driver-assistance system, road-sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detection phase, two neural networks are developed to extract color and shape features of traffic signs from the input scenes images. Traffic signs are then located in the images based on the extracted features. This process is primarily conceptualized in terms of fuzzy-set discipline. In the tracking phase, traffic signs located in the previous phase are tracked through image sequences using a Kalman filter. The experimental results demonstrate that the proposed method performs well in both detecting and tracking road signs present in complex scenes and in various weather and illumination conditions.     

Role of environmental and annealing conditions on the passivation-free in-Ga–Zn–O TFT

We examined the characteristics of passivation-free amorphous In–Ga–Zn–O thin film transistor (a-IGZO TFT) devices under different thermal annealing atmospheres. With annealing at higher temperature, the device performed better at the above-threshold operation region, which indicated the film quality was improved with the decrease of defects in the a-IGZO active region. The mobility, threshold voltage and subthreshold swing of a-IGZO TFT annealed at 450 °C was 7.53 cm²/V s, 0.71 V and 0.18 V/decade, respectively. It was also observed that the a-IGZO was conductive after thermal annealing in the vacuum, due to the ease of oxygen out-diffusion from the a-IGZO back channel. The oxygen deficiency resultantly appeared, and provided leaky paths causing electrical unreliability when TFT was turned off. In contrast, the annealing atmosphere full of O₂ or N₂ would suppress the oxygen diffusion out of the a-IGZO back channel. The worst V_th degradation of a-IGZO TFT after positive gate bias stress and negative gate bias stress (NGBS) was about 2 V and ? 2 V, respectively. However, the V_th shift in the NGBS testing could be suppressed to ? 0.5 V in vacuum chamber. Material analysis methods including X-ray photoelectron spectroscopy and scanning electron microscopy were used to investigate the change of a-IGZO film after different thermal annealing treatments. The variation of O 1s spectra with different annealing atmospheres showed the consistence with our proposed models.     

Micro-spike EEG electrode and the vacuum-casting technology for mass production

An innovative dry electroencephalography (EEG) electrode has been successfully designed and tested, in which multiple micro-spike electrodes, each of them consisting of a micro-pillar with a micro-tip on top of it, were designed to pass through the hairs and establish electrical conduction at the skin-electrode interface by penetrate into the stratum corneum of the skin. For hygiene reasons, such electrodes should be made disposable, at the same time, should be cost effective. Therefore, a mass production technology, including the processing methods, such as casting, has to be designed and developed. In this project, the micro-spike dry electrodes were fabricated by a vacuum casting method using a master pattern piece made by CNC micro-machining, in which silicone rubber moulds are created and then used to vacuum cast polyurethane (PU), epoxy or epoxy-carbon micro-spike electrodes. In order to obtain a harder polymeric material, varying amount of carbon fillers were added to the epoxy resin, and the hardness of the resulting material were measured and compared. It was found that a higher concentration of added carbon fillers resulted in a harder cast polymer composite. Further to the vacuum casting, to create an electrically conductive layer on the vacuum-casted electrode, an Ag/AgCl electroless deposition method has been developed. The sputtering of the conductive layer was also carried out for comparison. The developed micro-spike electrodes showed better performance in terms of the impedance level and stability as well as a much higher efficiency in EEG measurement.     

Micromachined plastic W-band bandpass filters

Results are presented for micromachined plastic waveguide bandpass iris filters for W-band applications using a cost-effective polymer micro hot embossing process in conjunction with metallic electroplating and sealing techniques. The prototype filter has an 8-μm thick electroplated gold layer on a polymeric WR-10 waveguide with a 5-cavity Chebyschev-type design. Measurement results show center frequency of 96.77 GHz with a bandwidth of 3.15%, a loaded quality factor 31.73 and an unloaded quality factor for a single cavity resonator is 1210.6, respectively. A minimum insertion loss of −1.22 dB and return loss of better than −9.3 dB have been measured over the entire passband.