Integrated Systems, Design and Technology 2010

Bridging veins are frequently damaged in traumatic brain injury. These veins are prone to rupture in their subdural portion upon head impact, due to brain-skull relative motion, giving rise to an acute subdural hematoma. To understand the biomechanical characteristics of this hematoma, we take into account the periodical distribution of bridging veins in the sagittal plane. This allowed the use of the method of homogenization by asymptotic expansion to calculate the homogenized elastic properties of the brain-skull interface region. The geometry of this zone was simplified and a representative volume element was developed comprising: sinus, bridging vein, blood circulating inside them, surrounding cerebrospinal fluid and tissues. The heterogeneous elementary cell is transformed to an anisotropic homogenous equivalent medium and the homogenized elastic properties were calculated. The macroscopic homogenized properties resulted from the current study can be incorporated into a finite element model of the human head at macroscopic scale. The main results of this homogenization theory are the calculation of the local stress field into the elementary cell, as well as its homogeneous anisotropic properties at the macroscopic scale.

Despite the success of surgical implants such as artificial hip, materials used in these procedures still do not satisfy the demands of human life time functioning. Currently used materials such as titanium alloys, ceramics and polymers are degraded after about a dozen years of use. Diamond coating technology has proven to be efficient in the performance of human joints. In this study, we have investigated the deposition of diamond thin films on Ti6Al4V using a new developed time-modulated Chemical Vapour Deposition (TMCVD) method. Finite element simulations were used to analyse the development of residual stresses. Micro Raman spectroscopy was also used to evaluate the residual stresses and compare with numerical models.

The natural resources available for national and international economic development are limited. A gentler and more efficient use of available energy and materials in all sectors is essential. In mobile applications in particular, in which large masses are moved and accelerated (e.g. automotive, railway, aircraft and in machinery and equipment), a consequent lightweight construction is necessary for a significant saving of energy.

The highest potential of lightweight constructions is in the field of fibre- and textile-fibre-reinforced composites. In some applications, a defined ductility is necessary. To achieve these requirements, high-strength and high-stiff carbon-fibre-reinforced plastics (CFRP) were combined with metal foils by means of hybrid laminates.

The focus of the current investigations is based on the development of hybrid laminates (CAPET and CAPAAL) which consist of metal foils (Ti- and Al-alloy foils) and layers of CFRP. By means of thermoplastic CFRP matrices (e.g. PA and PEEK), disadvantages of the thermosetting plastic matrix can be avoided. The excellent formability of the thermoplastic matrices should be named in particular. Within this contribution first results of the joint project will be presented.

Aerospace manufacturing often involves complex large components that are difficult to be assembled. The problems are usually associated with the undesired deformation of large structures when under the influence of jig conditions. Unpredictable pre-assembly manufacturing processes together with the presence of the unaccounted actions of the external forces during assembly often result in the production of geometric errors that exceed the allowable tolerances. A typical example is the assembly of Airbus wing box structure. Often the operative are required to forcefully bring parts together on the jig for joining where there are undesired misalignments. Additionally, the traditional aerospace assembly methods do not lend themselves to the requirements of high throughput, precision assemblies. This paper describes the methodology used to predict the positional variations of wingbox structure during assembly operation. The focus here is on the assembly of ribs under simulated laboratory condition.

A design of experiment (DoE) was performed on PMMA-(ammonium polyphosphate/melamine polyphosphate/titanium dioxide) ternary system for optimizing both fire-retardancy properties and thermal stability. The software JMP^® was used for this purpose. In poly (methyl methacrylate) (PMMA), progressive substitution of titanium dioxide (TiO₂) nanoparticles by melamine polyphosphate (MPP) led to the reduction of the peak of Heat Release Rate (pHRR), whereas the substitution with ammonium polyphosphate (APP) first led to the reduction of pHRR followed by an increase from 9wt% APP onwards. The presence of titanium dioxide led to the increase of the time to ignition (TTI) and thermal stability. Laser Flash Analysis (LFA) measurement showed the heat insulation effect (i.e. low thermal diffusivity) of the residues that have developed during combustion. PMMA-7.5%APP/7.5%TiO₂ showed the major increase of the time of combustion alongside with the decrease of pHRR due to the barrier effect. The high carbon monoxide amount released during the cone calorimeter experiment confirmed an incomplete combustion. That sample presented a ceramized structure and the formation of titanium pyrophosphate (TiP₂O₇) was detected by XRD measurements. TiP₂O₇ resulted from the reaction between APP and TiO₂ upon combustion.

Fibre-reinforced ceramics have a higher fracture toughness compared to monolithic ceramics. Therefore, they are an attractive material for lightweight structural components in high-temperature applications. The liquid-silicon infiltration (LSI) process is a cost-efficient manufacturing route for fibre-reinforced ceramics consisting of three processing steps. A carbon-fibre-reinforced plastic (CFRP) composite is fabricated, which is converted in a porous C/C composite by pyrolysis. Liquid silicon is infiltrated to form a dense C/C-SiC composite. The performance of the composites strongly depends on the raw materials. The matrix chemistry in particular plays a key role in developing composites with tailored functional properties. The aim of this work is to investigate the mechanical properties and the failure behaviour of CFRP, C/C and C/C-SiC composites in dependence on the matrix chemistry. The composites are fabricated by the liquid-silicon infiltration process. Different phenolic resins as matrix polymers are used. The mechanical properties are characterized by bending tests and the failure behaviour is observed in-situ. Additionally, microstructural and fractrographic analyses are done. It can be shown that the formation of the microstructure and therefore the mechanical properties and the failure behaviour are strongly influenced by the matrix chemistry over all processing steps.

The polymer melt impregnation of glass fibre tapes or rovings is of vital importance for manufacturing textile reinforced thermoplastic composites. In the present paper, the polymer melt impregnation of fibre bundles in injection moulding process is investigated by a specific pull-out test which allows the preparation of the investigated specimen near to process. Several glass fibre roving types with varying appearance and yarn count were placed as a tape insert in a mould for thermoplastic injection process and were impregnated with the injected poly-propylene melt. The fibre bundle pull-out test then was used to determine the quality of melt impregnation which could be verified by magnified photomicrograph. A clear relationship between maximal load in pull-out test and impregnated fibre fraction has been observed. Also a strong influence of the fibre bundle geometry and yarn count on impregnation quality was detected.

In performing an experimental analysis it is always important to take into account different parameters influencing the results. Boundary conditions in addition to the specimen size in case of nonlinear materials could be even more effective. Hence, a full analysis of these parameters before doing any test on the real material seems necessary. In this paper, these effects are numerically analyzed, using ANSYS APDL coding. Samples are cylindrical elastomers becoming ready for a relaxation test in order to get Prony series of the material. The influences of friction and applied displacement in addition to the area-length ratio have been investigated and the optimal values in order to do an appropriate relaxation test are proposed.

This paper deals with the production of aluminium matrix composites through high-energy milling, hot isostatic pressing and extrusion. Spherical powder of the aluminium alloy AA2017 (grain fraction > 100 μm) was used as matrix material. SiC and Al₂O₃ powders of submicron and micron grain size (< 2 μm) where chosen as reinforcement particles with contents between 5 and 15 vol.% respectively. The high-energy milling process was realised in a Simoloyer mill (Zoz). The milling time was about 4 hours. Hot isostatic pressing (HIP) was used to convert the compound powder into compact material. The extrusion process realises semi-finished products with different geometrical shapes.

The stages of the composite powder formation during high-energy ball milling will be discussed by means of metallographic studies. The SEM and TEM results show the microstructure of the compact composites obtained by HIP and extrusion. This study focuses on the dispersion and embedding of reinforcement particles and matrix/reinforcement interfaces. Typical appearances like ferrous contaminations through abrasion of the steel balls and the formation of the spinel phase MgAl₂O₄ during the subsequent powder-metallurgical processing will be discussed. The semi-finished product exhibits a good particle dispersion and low microporosity. There are no accumulations of the brittle phases nor any microcracks at the interface between reinforcement particles and matrix.

Based on an increasing demand for function integration in small components, micro injection moulding offers a highly productive solution to combine plastic structures with additional electronic and mechatronic features. Particularly two-component micro injection moulding allows embedding of active elements as well as the application of electric contacts in one process step by using insolating and conductive compounds, respectively. The study outlines major effects on the thermo-mechanical compatibility of active modules comprising several stacked piezo fibre composite beams. With regard to material properties, geometry of the module, machine set up and processing parameters a structural and strength analysis including process induced residual stress is used to predict favourable material combination and composite design.

To produce silver-based contact materials such as silver/tin oxide different ways are possible. All usual technologies have their typical advantages and disadvantages. Expensive techniques are characterised by a very fine reinforcement distribution. Techniques with a high output quantity display a very limited particle distribution within the matrix material.

By means of the economical mechanical alloying process, particle-reinforced metal-matrix composite powders with a fine distribution of the reinforcement within the matrix material can be produced. This contribution shows the morphology and microstructure of mechanically alloyed silver-based powders by means of the high-energy ball-milling process as well as the microstructure and mechanical properties of the consolidated materials. The objective of the present investigation has been the successful and reproducible production of particle-reinforced metal-matrix composites as well as the reduction of the noble-metal part by increasing the content of the reinforcement. Current investigations present a successful increase of the oxide content to 17 wt.-%.

Sporting goods such as snowboards have to resist enormous loads while using them. Especially freestyle snowboards are extremely stressed during the landing of high jumps or while sliding sideways on a rail. During such a board slide the steel edge of a snowboard tends to cut into the handrail and the motion of the athlete can be interrupted abruptly. This fact leads to crashes and can cause severe injuries. Catching the edge is nearly eliminated by using an all-new anisotropic layer design (ALD)-snowboard which has been developed, simulated and tested by the Professorship of Lightweight Structures and Polymer Technology (SLK) in cooperation with the Professorship of Sports Equipment and Sports Technology (SGT).

The potential of lightweight construction with composite materials is frequently not fully tapped. The reason is that the complex anisotropic properties are not fully reflected in the design. Instead, to be on the safe side, more or thicker material has been used. The numerical structural optimization is a way to calculate the optimal load-dependent material distribution or fibre orientation in complex fibre-reinforced components. This method was used to design the chassis of the ultra-lightweight, extremely energy-saving vehicle “Sax 3” of the student project “fortis saxonia” for the Shell Eco-marathon. Thus it has become possible to keep the weight of the chassis under 10 kg. This shows the high potential of the implementation of this optimization approach for fibre-reinforced composites.

The use of embedded sensors and actuators for condition assessment of machines and structures is on rising demand [1]. The load monitoring of parts made of composite materials, known as structural health monitoring, is of growing interest. Certainly, composite parts with high demands on safety and a long economic life-time, e. g. parts in airplanes and blades of wind turbines, will be equipped with strain- and impact-sensitive sensors in future. The monitoring will lead to a better understanding and a safer application of composite materials. Different types of sensors based on different physical effects are available.

The diameter or size of all of these available strain sensors is bigger than fibres in composite materials, and thus, these sensors do influence the structure and adulterate the strain measurements. Therefore, the aim of this work is to develop a strain sensor with a diameter equal to reinforcing fibres. Carbon fibre surfaces coated with magnetoelastic materials and structured by methods of micromanufacturing can serve as strain- and tension-sensitive microsensor systems capable of supporting safety and health monitoring functions in parts made of composite materials. The formerly developed fabrication method [3] based on thin-film deposition technology (cathode sputtering, plasma-enhanced chemical vapour deposition) [4] and microstructuring by means of focused ion beam (FIB) machining has been optimised. A novel PVD technique has been developed to deposit homogenous layers on single fibres and to improve the layer quality. Certainly, the deposition of magnetoelastic materials has been optimised. First sensor prototypes have been integrated into carbon-fibre-reinforced plastics. The results of all examinations will be presented.

In this work, we discuss challenges and opportunities facing PEM fuel cell systems in three levels: system architecture, control, and diagnostics. In the system architecture context, we analyze various realizations for the fuel cell system as well as the hybrid system. In the control context, we discuss the optimal control strategy for flow, humidity, temperature, and power subsystems. In the diagnostic context, we discuss diagnostic tools based on the stage in the product lifecycle where they are required, i.e., research, development, manufacturing, and operation.

The Fuel Cell Laboratory at Florida Atlantic University (FAU) originally founded to support a research funded by the US Department of Energy under the leading author’s supervision during 1996-1997 period [1]. In sequel, the laboratory has been upgraded with several industrial funding supports, including Teledyne Inc., Enerfuel Inc, in addition to the National Science Foundation (NSF) and FAU internal research funds. With the continuing support of local FC industries, the laboratory is considered a focal point for the research and educational activities pertaining to proven fuel cell testing technology. In this paper, an overview of several research and instructional activities of FAU Fuel Cell Laboratory are briefly presented.

The past few years have witnessed a proliferation of industrial and decision making applications of a novel neurocontroller designated as BELBIC: Brain Emotional Learning Based Intelligent Controller. Based on a proposed open loop descriptive model of the midbrain, where emotional processing is understood to mainly take place, its utilization has been motivated by the belief that most human decisions are made using bounded rationality. Successful control engineering and decision making applications are reviewed, where BEL has been used for satisficing action selection based on artificial emotions. Laboratory and Industrial scale applications are emphasized. Recent results on stability and performance guarantees are also examined. Finally flexible bioinspired SOC and other hardware/software implementations of BELBIC are investigated. It is argued that in order for VLSI implementations of neural networks to be commercially viable, it is crucial to minimize the redesign expenses for their optimal dedicated implementations for any given application on any desired platform. Furthermore, on line applications require recursive learning that need not precede the recall mode. High levels of adaptability, disturbance rejection, and fault tolerance are other important characteristics of the proposed IC.

Creating a highly programmable surface operating at relatively high speed and in real time is an area of research with many challenges. Such a system has applications in the field of optical telescopes, product manufacturing, and giant 3D-screens and billboards for advertising and artwork. This paper covers certain aspects of a keynote presentation at ISDT 2010 including system design, modularity, programmability and the system control intelligence. An overview of the system architecture, actuator design, electronics and distributed control will provide an insight into how the system is controlled and self-tuned for a number of applications. A simulation environment that has been developed to streamline system reconfiguration will also be presented, demonstrating translation of complex mathematical functions into 3D shapes virtually before being displayed on the physical surface.

This paper develops a general framework for assessment and management of competence. It then illustrates a case study demonstrating how to pragmatically assist engineers and managers to confirm their competence, knowledge and understanding against occupational standards without placing undue pressure on their time. It proposes a form of continuous assessment over a 3-6 month period using electronic evidence provided by the candidate in response to a set of focussed emailed questions to build up a paperless portfolio. It also briefly looks how the process can be extended to maintain and update competence and possible future steps to quantify the assessed competence based on weighted performance measures.

This paper presents a novel wavelet-based methodology for fault diagnosis and classification. To compare the performance of the proposed approach with major existing methods, various sets of real-world machine data acquired by mounting accelerometer sensors on the cylinder head have been extensively tested. The developed method not only avoids the demerits of the previous techniques but also demonstrates superior performance.

Many modern high-tech products gain their functionality from structured nanoscale layers. This is the case for e.g.nanoelctronic circuits, MEMS and NEMS sensors and actuators, photovoltaic cells, but also macroscopic structures with functional nanolayer coating. The production of these nanolayers is accomplished by chemical/physical processes performed in clean rooms under extremely controlled conditions. IT support for nanolayer production is currently available for the fabrication machinery on the one hand (MES - Manufacturing Execution Systems) and for the global controlling of enterprise operations (ERP - Enterprise Resource Planning) on the other hand. There is a gap between these two areas covering the whole field of project and product related planning, design, optimization and verification of a specific fabrication flow. Currently first PDES (Process development execution Systems) are available to close this gap and hence achieve a holistic enterprise information management (EIM). This article will give a brief introduction to EIM for nanolayered devices and will then present the benefits of PDES systems by the example of the first comprehensive solution in this field – the XperiDesk suite. This software system has originally been designed at Siegen University and is now available on the market place from ProcessRelations GmbH in Dortmund, Germany.

The Web is the world’s largest data repository, and search engines are regarded as key tools for finding and extracting useful information from the tremendous amount of available data. In recent years, user-oriented Information Seeking (IS) research methods rooted in the social sciences have been integrated with computer science-based Information Retrieval (IR) approaches to capitalize on the strengths of both fields. The concept is called the Information Seeking and Retrieval (IS&R) framework. Pilot research models in the IS research area show that workers’ information seeking activities exhibit common patterns. In this study, we will systematically identify and categorize important theoretical frameworks of IS&R. Based on the observations of IS&R studies, we propose implicit and explicit evolving topic-needs determination methods. The methods consider personal factors, and content factors simultaneously in order to fulfil the user’s evolving information needs precisely. The research contributes to design the retrieval functions based on the IS&R models for supplying documents for knowledge-intensive tasks.

Current manufacturing processes are characterized by their high complexity and require an increased control effort. Operating them effectively and efficiently is crucial and knowledge integration methods can make a valuable contribution to this. Presented here is a generic model predictive system that enables the integration of different sources of knowledge. In addition, the system is adaptive and allows for a self-adaptation to changing operating conditions and a self-optimization. The implementation of an inferential control mechanism finally ensures continuous process control in the absence of primary measurements.

Control of an unknown nonlinear time-varying plant has always been a great concern for control specialists, thus an appealing subject in this discipline. Many efforts have been dedicated to explore the various aspects of this problem. This research has led into introducing many new fields and methods. These methods can be categorized into two general classes as: data-driven and model-driven. Model driven methods in spite of having rigorous analytical basis are not employed as frequently as data-driven ones. On the other hand data-driven methods are suitable to be employed in nonlinear and dual controller design; however they are slightly unsuccessful in handling missing data, moreover these methods are in need of considerable amount of computation. This paper introduces a novel hybrid nonlinear controller which aggregates Gaussian Process Prior as a data-driven and a Fuzzy Controller as a model-driven method .This special structure brings model-driven and data-driven advantages altogether, thus naturally lead into a robust and adaptive controller. Since no prior knowledge of the plant is used to issue the control law, the proposed hybrid controller can also be regarded as a dual controller.

Modern steel products are manufactured using many different production steps. One step is the hardening by heat treatment. To assess the quality of the processed steel products, the microstructure can be analyzed. This article describes all necessary steps, beginning from image capturing from microscopes to the classification using fuzzy logic based classifiers.

Usage of electronics and sensors in modern vehicles is constantly increasing. The quality demands of the automotive supply chain require complex manufacturing execution systems (MES) and data analysis tools (data mining). On one hand, the constant demand for increase in efficiency leads to bigger wafer sizes and more automation in the fabs. On the other hand this causes problems with products of small quantities, where the meaning of “small” is continuously rising with wafer size and automation. New products such as MEMS devices and microsystems increase these problems, because the processes must here also be adapted to the products.

The established tools are not sufficient to comply with the continuously increasing demands. The use of modern production control systems and methods are thus presented using the ELMOS group as an example.

This paper describes the evaluation of skill and kansei of professionals for ship handling toward practical education using thier skill and kansei. We evaluate the professionals’ performance using a ship bridge simulator; moreover, we ascertain their mental workload on board by a training ship. We show the effect of physiological indices from the professionals’ performance and the characteristics of their mental workload with heart rate variability (R-R interval), nasal temperature and salivary amylase activity. We confirm whether the response to their performance for the ship handling is clear or not. Mental workload is useful to evaluate performance of ship bridge teammates: a captain, a duty officer, a helmsman, and a pilot.

Time series prediction has been used widely in engineering, finance, economy, traffic and many other areas. It is an important tool in complex system identification. Many methods for identifying nonlinear systems by local linear model have been introduced; one of the most important methods used in these fields is LoLiMoT, which is an incremental learning algorithm. Wavelet analysis breaks the original signal into two parts: details and approximation. Wavelet analysis gives ability of using the prolonged intervals where more accurate low-frequency information is needed. Shorter intervals are used when we analize high-frequency information. In this paper an efficient method is presented to enhance accuracy of time series prediction that combines wavelet decomposition and LoLiMoT. The experimental results demonstrate the enhancement of accuracy of proposed method over the data set of sunspot number time series.