Effect of the manufacturing process on the interlaminar fracture toughness of 2/2 twill weave fabric carbon/epoxy composites

A 2/2 twill weave fabric carbon fibre reinforced epoxy matrix composite MTM56/CF0300 was used to investigate the effect of different manufacturing processes on the interlaminar fracture toughness. Double cantilever beam tests were performed on composites manufactured by hot press, autoclave and 'Quickstep' processes. The 'Quickstep' process was recently developed in Perth, Western Australia for the manufacture of advanced composite components. The values of the mode I critical strain energy release rate (G1d were compared and the results showed that the composite specimens manufactured by the autoclave and the 'Quickstep' process had much higher interlaminar fracture toughness than the specimen produced by the hot press. When compared to specimens manufactured by the hot press, the interlaminar fracture toughness values of the Quickstep and autoclave samples were 38% and 49% higher respectively. The 'Quickstep' process produced composite specimens that had comparable interlaminar fracture toughness to autoclave manufactured composites. Scanning electron microscopy (SEM) was employed to study the topography of the mode I interlaminar fracture surface and dynamic mechanical analysis (DMA) was performed to investigate the fibre/matrix interphase. SEM micrography and DMA spectra indicated that autoclave and 'Quickstep' produced composites with stronger fibre/matrix adhesion than hot press.

Prediction based location aided routing for mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided Routing (P-LAR) protocol. Simulation study and analytical results of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Mobility prediction based routing for minimizing control overhead in mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided routing (P-LAR) protocol. Simulation study and analytical results of the of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Address reuse in wireless sensor networks

Sensor Networks have applications in diverse fields. While unique addressing is not a requirement of many data collecting applications of wireless sensor networks, it is vital for the success of applications such as emergency response. Data that cannot be associated with a specific node becomes useless in such situations. In this work we propose a dynamic addressing mechanism for wireless sensor networks. The scheme enables successful reuse of addresses in event-driven wireless sensor networks. It also eliminates the need for network-wide Duplicate Address Detection (DAD) to ensure uniqueness of network level addresses.

Using autonomous mobile agents for efficient data collection in sensor networks

Sensor networks are emerging as the new frontier in sensing technology, however there are still issues that need to be addressed. Two such issues are data collection and energy conservation. We consider a mobile robot, or a mobile agent, traveling the network collecting information from the sensors themselves before their onboard memory storage buffers are full. A novel algorithm is presented that is an adaptation of a local search algorithm for a special case of the Asymmetric Traveling Salesman Problem with Time-windows (ATSPTW) for solving the dynamic scheduling problem of what nodes are to be visited so that the information collected is not lost. Our algorithms are given and compared to other work.

Novel data management algorithms in peer-to-peer content distribution networks

The peer-to-peer content distribution network (PCDN) is a hot topic recently, and it has a huge potential for massive data intensive applications on the Internet. One of the challenges in PCDN is routing for data sources and data deliveries. In this paper, we studied a type of network model which is formed by dynamic autonomy area, structured source servers and proxy servers. Based on this network model, we proposed a number of algorithms to address the routing and data delivery issues. According to the highly dynamics of the autonomy area, we established dynamic tree structure proliferation system routing, proxy routing and resource searching algorithms. The simulations results showed that the performance of the proposed network model and the algorithms are stable.

Route maintenance using mobility prediction for mobile ad hoc networks

Next generation networks (3G & beyond) will support real-time multimedia applications through traditional wide-area networking concepts as well as hot-spot (WLAN) and ad hoc networking concepts. In order to fulfill the vision of next generation networks a method of maintaining a real-time flow despite frequent topology changes and irregularity in user movement is required. Mobility prediction has been identified as having applications in the areas of link availability estimation and pro-active routing in ad hoc networks. In this work we present the mobility prediction based algorithm for route maintenance in mobile ad hoc networks. Simulation study of the algorithm proves it to offer significant benefits to dynamic source routing (DSR)

Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller

The main objective of a steel strip rolling process is to produce high quality steel at a desired thickness.  Thickness reduction is the result of the speed difference between the incoming and the outgoing steel strip and the application of the large normal forces via the backup and the work rolls.  Gauge control of a cold rolled steel strip is achieved using the gaugemeter principle that works adequately for the input gauge changes and the strip hardness changes.  However, the compensation of some factors is problematic, for example, eccentricity of the backup rolls.  This cyclic eccentricity effect causes a gauge deviation, but more importantly, a signal is passed to the gap position control so to increase the eccentricity deviation.  Consequently, the required high product tolerances are severely limited by the presence of the roll eccentricity effects.
In this paper a direct model reference adaptive control (MRAC) scheme with dynamically constructed neural controller was used.  The aim here is to find the simplest controller structure capable of achieving an optimal performance.  The stability of the adaptive neural control scheme (i.e. the requirement of persistency of excitation and bounded learning rates) is addressed by using as the inputs to the reference model the plant's state variables.  In such a case, excitation is due to actual plant signals (states) affected by plant disturbances and noise.  In addition, a reference model in the form of a filter with a desired transfer function using Modulus Optimum design was used to ensure variance in the desired dynamic characteristics of the system.  The gradually decreasing learning rate employed by the neural controller in this paper is aimed at eliminating controller instability resulting from over-aggressive control.  The moving target problem (i.e. the difficulty of global neural networks to perfrom several separate computational tasks in closed -loop control) is addressed by the localized architecture of the controller.  The above control scheme and learning algorithm offers a method for automatic discovery of an efficient controller.
The resulting neural controller produces an excellent disturbance rejection in both cases of eccentricity and hardness disturbances, reducing the gauge deviation due to eccentricity disturbance from 33.36% to 4.57% on average, and the gauge deviation due to hardness disturbance from 12.59% to 2.08%.

Manufacturing process effects on the mode I interlaminar fracture toughness and nanocreep properties of CFRP

Quickstep ™ is a fluid filled floating mould technology which was recently developed by an Australian company of the same name. The Quickstep and conventional autoclave manufacture of composites were compared by investigating the mode I interlaminar fracture toughness and nanocreep propeties of HexPly914 carbon epoxy composites. It was found that composites cured using the Quickstep technology had significantly higher fracture toughness (1.8 times) than the composites cured via autoclave for this system. DMTA (dynamic mechanical thermal analysis) results showed a higher Tg (glass transition temperature) for the material manufactured by the Quickstep than that cured by the autoclave. FTIR (Fourier transform infrared spectroscopy) spectra did not indicate any difference in cure chemistry between the two processes. Nanocreep experiments were performed to explore the viscoelastic properties of the epoxy matrix of composites. The KelvinVoigt three-element model was applied to analyse the indentation creep behaviour of both composites.

Design and degradation modelling through artificial neural networks

Automotive is one of the major manufacturing industries in Australia that requires extensive reliability test for the components used in vehicles. To achieve a shorter time-to-market and a highly reliable product while reducing the amount of physical prototyping, there is a growing need for better understanding on the effect that the design parameters have on the degradation of the product. This paper presents comprehensive descriptions of applying Artificial Neural Network (ANN) to capture the relationships between design and degradation. Consequently, two models of different practical significance are created as the result of the work. The vision of the models is to be used by the testers and designers as a guideline in design evaluation, so that time-consuming and expensive iterations of the product developmental cycle can be reduced substantially. The degradation of the folding force of a mechanical system is used to illustrate our approach.

Input and output analysis of complex conveyor networks

Material transfer networks are at the heart of critical infrastructure in many modern service and manufacturing industries. This research identified key performance measures, while deriving generalised analysis methodologies, for simulation models. The technology was validated for international airports, and used to determine operational capacity constraints under varied demand conditions.

Fast recovery from node compromise in wireless sensor networks

Wireless Sensor Networks (WSNs) are susceptible to a wide range of security attacks in hostile environments due to the limited processing and energy capabilities of sensor nodes. Consequently, the use of WSNs in mission critical applications requires reliable detection and fast recovery from these attacks. While much research has been devoted to detecting security attacks, very little attention has been paid yet to the recovery task. In this paper, we present a novel mechanism that is based on dynamic network reclustering and node reprogramming for recovering from node compromise. In response to node compromise, the proposed recovery approach reclusters the network excluding compromised nodes; thus allowing normal network operation while initiating node recovery procedures. We propose a novel reclustering algorithm that uses 2-hop neighbourhood information for this purpose. For node reprogramming we propose the modified Deluge protocol. The proposed node recovery mechanism is both decentralized and scalable. Moreover, we demonstrate through its implementation on a TelosB-based sensor network testbed that the proposed recovery method performs well in a low-resource WSN.

Multi-market industrial organizational economic models for the internationalization process by small and medium enterprise construction design service firms

Small and medium enterprises (SMEs) are critical to strategic initiatives in an economy; however, their contribution to foreign trade is not as significant. SMEs are one of the principal driving forces in economic development. One of the greatest challenges is the internationalization process for longevity rather than seeing the process as initial market entry. The internationalization process research has typically involved four key constructs: market selection, decision to enter, entry modes and factors affecting entry modes. Past research has focused on large manufacturing firms. The export of architectural, engineering and construction (AEC) firms has undergone growth, yet there is still significant opportunity for further growth. The majority of AEC firms are SMEs. Notwithstanding assistance provided through international trade missions, organized export firm support networks and information packages by a burgeoning number of government agencies, there are still perceived barriers to market entry and long-term economic sustainability for SMEs. There are a number of problems faced by SMEs acting in foreign trade. This investigation explores the successful initial internationalization process constructs and identifies unique project-oriented sector characteristics. The study identified similarities and differences between two firms that have been exporting to various localities, including Eastern Europe, Africa, Middle East, UK, Asia and South America, for more than two decades. The similarities and differences were identified within eight major constructs: purpose, firm type, market image and design philosophy, entry mode strategy, institutional arrangement, factors affecting mode of entry, market selection and firm strategy in relation to project selection. The primary reasons for internationalization were associated with the firms' motivations related to growth and financial viability. This article discusses the various internationalization processes and strategies intrinsic to each case study and establishes a detailed set of empirical observations from which to develop further a grounded theoretical model of reflexive capability for the internationalization process. This study contributes to the body of knowledge around the SME AEC design service firm's internationalization process, as a dynamic, evolving and continuously adaptable construct for project-based sectors.

Adaptive data based neural network leader-follower control of multi-agent networks

In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

A simple motion heuristic for controlling mobile sinks in delay tolerant sensor networks

Optimising energy consumption in wireless sensor networks is of dominant importance. Sink mobility is introduced to deal with this problem by approaching the sensor nodes and collecting their data buffers using the less energy demanding single-hop communication. The sink route is very crucial for the data collection operation performed in the network especially when the collection requests generated by the sensors are revealed dynamically to the sink and not known ahead. This paper presents a practical motion heuristic for constructing the sink route based on the dynamic arrival of the collection requests. Three control schemes are proposed for coordinating the interaction of multiple mobile sinks collectively performing the data collection in the network. The main objective is maximising the data collected by each mobile sink while minimising the sleeping time of each sensor awaiting the collection service. Simulation results show the performance of the mobile sinks under the proposed control schemes and the impact of the motion heuristic on the sensors' sleeping time in the network.