Rotational molding cycle time reduction through surface‐enhanced molds,Part B: Experimental study

Rotational molding is a process used to manufacture hollow plastic products, and has been heralded as a molding method with great potential. Because of long production cycle times, which are limited by the time required to heat‐up and cool‐down the mold and the product, its productivity is hampered. To address this issue, exterior mold modification techniques (i.e. the application of extended and rough surfaces) have been employed to enhance heat transfer to and from molds, ultimately reducing cycle times. Extended surfaces have the potential to enhance heat transfer by increasing the surface area. Roughness elements are utilized in conjunction with turbulent flows, also producing significant increases in heat transfer rates. Experimental results presented here demonstrate very significant cycle time reductions through the use of surface‐enhanced molds. The experimental savings are in the order of 18 and 28%, whereas the predicted cycle time reductions are around of 21 and 32% for roughness‐enhanced and pin‐enhanced molds, respectively. Although the prediction methods have been unable to forecast the exact experimental cycle times very accurately, they have proved to be useful for predicting the approximate cycle time reductions and the relative rankings of the plain and the surface‐enhanced molds. POLYM. ENG. SCI., 47:1420–1429, 2007. © 2007 Society of Plastics Engineers     

Tool wear prediction models during end milling of glass fibre-reinforced polymer composites

Composite products are often subjected to secondary machining processes as integral part of component manufacture. However, rapid tool wear becomes the limiting factor in maintaining consistent machining quality of the composite materials. Hence, this study demonstrates the development of an indirect approach in predicting and monitoring the wear on carbide tool during end milling using multiple regression analysis (MRA) and neuro-fuzzy modelling. Although the results have indicated that acceptable predictive capability can be well achieved using MRA, the application of neuro-fuzzy yields a significant improvement in the prediction accuracy. It is apparent that the accuracies are pronounced as a result of nonlinear membership function and hybrid learning algorithms. Using the developed models, a timely decision for tool re-conditioning or tool replacement can be achieved effectively.     

Cohesion: A concept and framework for confident association discovery with potential application in microarray mining

The minimal frequency constraint in classical association mining algorithms turns out to be a challenging bottleneck in discovery of large number of infrequent associations that can be potential in knowledge content. A lower choice for threshold frequency not only incurs huge cost of pattern explosion but also cuts reliability of discovered knowledge. The goal of the present paper is to devise a new framework addressing two necessities. The first is discovery of confident associations unconstrained to classical minimal frequency. The second is to ensure quality of the discovered rules. We propose a new property among items, terming it cohesion, and develop cohesion-based scalable algorithms for confident association discovery. In order to assess quality of rules in terms of knowledge content, we propose two new measures, accuracy and predictability based on documented associations. Experiments with market-basket data as well as microarray data establish superiority of cohesion-based technique both in terms of amount and quality of discovered knowledge.     

High gas dependence for power generation in Thailand: The vulnerability analysis

Thailand uses 74% of its natural gas supply for power generation and 70% of its power comes from gas-based technology. High dependence on natural gas in power generation raises concerns about security of electricity supply that could affect competitiveness of Thai manufacturing and other industries at the global level. The effect of fuel dependence on security of electricity supply has received less emphasis in the literature. Given this gap, this research examines the economic impact of high dependence on natural gas for power generation in Thailand by analyzing the effect of changes in fuel prices (including fuel oil and natural gas) on electricity tariff in Thailand. At the same time, the research quantifies the vulnerability of the Thai economy due to high gas dependence in power generation. Our research shows that for every 10% change in natural gas price, electricity tariff in Thailand would change by 3.5%. In addition, we found that the gas bill for power generation consumed between 1.94% and 3.05% of gross domestic product (GDP) between 2000 and 2004 and in terms of GDP share per unit of energy, gas dependence in power generation is almost similar to that of crude oil import dependence. We also found that the basic metal industry, being an electricity intensive industry, is the most affected industry. Additionally, we find that volatility of gas price is the main factor behind the vulnerability concern. The research accordingly simulates two mitigation options of the problem, namely reducing gas dependence and increasing efficiency of gas-fired power plants, where the results show that these methods can reduce the vulnerability of the country from high gas dependence in power generation.     

Parametric control for optimal quality of the workpiece surface in ECM

The authors have successfully designed and developed an Electrochemical Machining set-up with a microprocessor-controlled stepper motor drive control unit for providing variable and automatic tool feed rates and also with an electronic circuit for auto-sensing of changes of the ECM gap condition during the course of machining, so as to actuate the auto-feedback control of the tool feed-rate and thus secure constant current Electrochemical Machining.

The present paper also highlights various research results with the auto-feed ECM system for analysing the effect of some of the process variables, e.g. the electrolyte flow rate, the electrolyte concentration, the current density and the applied voltage, on the various surface-roughness parameters and bearing properties of the machined surface, as measured with the help of a computerised Talysurf unit. The test results indicate clearly the optimal parametric combinations that are needed for enhanced metal removal and better surface finish and other surface topographical characteristics, with the present design of auto-tool-feed control. It is evident from the test results that the present study on ECM will be quite useful and a step forward for proceeding with further applied research for achieving effective utilisation of ECM in practice, with better surface-quality characteristics so as to meet the needs of modern manufacturing industry.     

APGAN and RPMC: Complementary Heuristics for Translating DSP Block Diagrams into Efficient Software Implementations

Dataflow has proven to be an attractive computational model for graphical DSP design environments that support the automatic conversion of hierarchical signal flow diagrams into implementations on programmable processors. The synchronous dataflow (SDF) model is particularly well-suited to dataflow-based graphical programming because its restricted semantics offer strong formal properties and significant compile-time predictability, while capturing the behavior of a large class of important signal processing applications. When synthesizing software for embedded signal processing applications, critical constraints arise due to the limited amounts of memory. In this paper, we propose a solution to the problem of jointly optimizing the code and data size when converting SDF programs into software implementations.We consider two approaches. The first is a customization to acyclic graphs of a bottom-up technique, called pairwise grouping of adjacent nodes (PGAN), that was proposed earlier for general SDF graphs. We show that our customization to acyclic graphs significantly reduces the complexity of the general PGAN algorithm, and we present a formal study of our modified PGAN technique that rigorously establishes its optimality for a certain class of applications. The second approach that we consider is a top-down technique, based on a generalized minimum-cut operation, that was introduced recently in [14]. We present the results of an extensive experimental investigation on the performance of our modified PGAN technique and the top-down approach and on the trade-offs between them. Based on these results, we conclude that these two techniques complement each other, and thus, they should both be incorporated into SDF-based software implementation environments in which the minimization of memory requirements is important. We have implemented these algorithms in the Ptolemy software environment [5] at UC Berkeley.     

A functional telomerase RNA swap in vivo reveals the importance of nontemplate RNA domains

The ribonucleoprotein (RNP) enzyme telomerase is required for replication of eukaryotic chromosomal termini. The RNA moiety of telomerase is essential for enzyme function and provides the template for telomeric DNA synthesis. However, the roles of its nontemplate domains have not been explored. Here we demonstrate that a novel interspecies telomerase RNA swap in vivo creates a functional but aberrant telomerase. Telomerase RNA from the ciliate Glaucoma chattoni was expressed in Tetrahymena thermophila cells. The telomerase RNAs from these two species have almost superimposable secondary structures. The template region base sequence is identical in the two RNAs, but elsewhere their sequences differ by 49%. This hybrid telomerase RNP was enzymatically active but added only short stretches of telomeric repeat tracts in vivo and in vitro. This new enzyme also had a strong, aberrant DNA cleavage activity in vitro. Thus, molecular interactions in the RNP involving nontemplate RNA domains affect specific aspects of telomerase enzyme function, raising the possibility that they may regulate telomerase activity.     

Nanoscale circuit implementation using tri-metal gate engineered nanowire MOSFET with gate stack for analog/RF applications

In this work, the potential benefit of tri-metal gate engineered nanowire MOSFET with gate stack for analog/RF applications is developed and presented. A systematic, quantitative investigation of main figure of merit for the device is carried out to demonstrate its improved RF/analog performance. The results show an improvement in drain current, \(I_{\mathrm{on}} /I_{\mathrm{off}}\) ratio, transconductance, unity-gain frequency (\(f_{\mathrm{T}}\)), maximum oscillation frequency (\(f_{\mathrm{max}}\)) providing superior RF performance as compared to single and dual-metal gate stack nanowire MOSFET. The suitability of the device for analog/RF applications is also analyzed by implementing the device in a low-noise amplifier circuit, and the S-parameter values are estimated.