The Performance Characteristics of a Surface-Modified Cutting Tool

In the past, many papers have been presented which show that the coating of cutting tools often yields decreased wear rates and reduced coefficients of friction. Although different theories are proposed, covering areas such as hardness theory, diffusion barrier theory, thermal barrier theory, and reduced friction theory, most have not dealt with the question of how and why the coating of tool substrates with hard materials such as Titanium Nitride (TiN), Titanium Carbide (TiC) and Aluminium Oxide (Al203) transforms the performance and life of cutting tools. This project discusses the complex interrelationship that encompasses the thermal barrier function and the relatively low sliding friction coefficient of TiN on an undulating tool surface, and presents the result of an investigation into the cutting characteristics and performance of EDMed surface-modified carbide cutting tool inserts. The tool inserts were coated with TiN by the physical vapour deposition (PVD) method. PVD coating is also known as Ion-plating which is the general term of the coating method in which the film is created by attracting ionized metal vapour in this the metal was Titanium and ionized gas onto negatively biased substrate surface. Coating by PVD was chosen because it is done at a temperature of not more than 5000C whereas chemical Vapour Deposition CVD process is done at very high temperature of about 8500C and in two stages of heating up the substrates. The high temperatures involved in CVD affects the strength of the (tool) substrates. In this study, comparative cutting tests using TiN-coated control specimens with no EDM surface structures and TiN-coated EDMed tools with a crater-like surface topography were carried out on mild steel grade EN-3. Various cutting speeds were investigated, up to an increase of 40% of the tool manufacturer’s recommended speed. Fifteen minutes of cutting were carried out for each insert at the speeds investigated. Conventional tool inserts normally have a tool life of approximately 15 minutes of cutting. After every five cuts (passes) microscopic pictures of the tool wear profiles were taken, in order to monitor the progressive wear on the rake face and on the flank of the insert. The power load was monitored for each cut taken using an on-board meter on the CNC machine to establish the amount of power needed for each stage of operation. The spindle drive for the machine is an 11 KW/hr motor. Results obtained confirmed the advantages of cutting at all speeds investigated using EDMed coated inserts, in terms of reduced tool wear and low power loads. Moreover, the surface finish on the workpiece was consistently better for the EDMed inserts. The thesis discusses the relevance of the finite element method in the analysis of metal cutting processes, so that metal machinists can design, manufacture and deliver goods (tools) to the market quickly and on time without going through the hassle of trial and error approach for new products. Improvements in manufacturing technologies require better knowledge of modelling metal cutting processes. Technically the use of computational models has a great value in reducing or even eliminating the number of experiments traditionally used for tool design, process selection, machinability evaluation, and chip breakage investigations. In this work, much interest in theoretical and experimental investigations of metal machining were given special attention. Finite element analysis (FEA) was given priority in this study to predict tool wear and coating deformations during machining. Particular attention was devoted to the complicated mechanisms usually associated with metal cutting, such as interfacial friction; heat generated due to friction and severe strain in the cutting region, and high strain rates. It is therefore concluded that Roughened contact surface comprising of peaks and valleys coated with hard materials (TiN) provide wear-resisting properties as the coatings get entrapped in the valleys and help reduce friction at chip-tool interface. The contributions to knowledge: a. Relates to a wear-resisting surface structure for application in contact surfaces and structures in metal cutting and forming tools with ability to give wear-resisting surface profile. b. Provide technique for designing tool with roughened surface comprising of peaks and valleys covered in conformal coating with a material such as TiN, TiC etc which is wear-resisting structure with surface roughness profile compose of valleys which entrap residual coating material during wear thereby enabling the entrapped coating material to give improved wear resistance. c. Provide knowledge for increased tool life through wear resistance, hardness and chemical stability at high temperatures because of reduced friction at the tool-chip and work-tool interfaces due to tool coating, which leads to reduced heat generation at the cutting zones. d. Establishes that Undulating surface topographies on cutting tips tend to hold coating materials longer in the valleys, thus giving enhanced protection to the tool and the tool can cut faster by 40% and last 60% longer than conventional tools on the markets today.

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

Time domain decomposition for European options in financial modelling

Finance is one of the fastest growing areas in modern applied mathematics with real world applications. The interest of this branch of applied mathematics is best described by an example involving shares. Shareholders of a company receive dividends which come from the profit made by the company. The proceeds of the company, once it is taken over or wound up, will also be distributed to shareholders. Therefore shares have a value that reflects the views of investors about the likely dividend payments and capital growth of the company. Obviously such value will be quantified by the share price on stock exchanges. Therefore financial modelling serves to understand the correlations between asset and movements of buy/sell in order to reduce risk. Such activities depend on financial analysis tools being available to the trader with which he can make rapid and systematic evaluation of buy/sell contracts. There are other financial activities and it is not an intention of this paper to discuss all of these activities. The main concern of this paper is to propose a parallel algorithm for the numerical solution of an European option. This paper is organised as follows. First, a brief introduction is given of a simple mathematical model for European options and possible numerical schemes of solving such mathematical model. Second, Laplace transform is applied to the mathematical model which leads to a set of parametric equations where solutions of different parametric equations may be found concurrently. Numerical inverse Laplace transform is done by means of an inversion algorithm developed by Stehfast. The scalability of the algorithm in a distributed environment is demonstrated. Third, a performance analysis of the present algorithm is compared with a spatial domain decomposition developed particularly for time-dependent heat equation. Finally, a number of issues are discussed and future work suggested.

Multi-physics modelling - A vital component of virtual manufacturing

One of the core tasks of the virtual-manufacturing environment is to characterise the transformation of the state of material during each of the unit processes. This transformation in shape, material properties, etc. can only be reliably achieved through the use of models in a simulation context. Unfortunately, many manufacturing processes involve the material being treated in both the liquid and solid state, the trans-formation of which may be achieved by heat transfer and/or electro-magnetic fields. The computational modelling of such processes, involving the interactions amongst various interacting phenomena, is a consider-able challenge. However, it must be addressed effectively if Virtual Manufacturing Environments are to become a reality! This contribution focuses upon one attempt to develop such a multi-physics computational toolkit. The approach uses a single discretisation procedure and provides for direct interaction amongst the component phenomena. The need to exploit parallel high performance hardware is addressed so that simulation elapsed times can be brought within the realms of practicality. Examples of Multiphysics modelling in relation to shape casting, and solder joint formation reinforce the motivation for this work.

Dynamic load balancing of distributed memory parallel computational mechanics using unstructured meshes for multi-physical modelling

As the complexity of parallel applications increase, the performance limitations resulting from computational load imbalance become dominant. Mapping the problem space to the processors in a parallel machine in a manner that balances the workload of each processors will typically reduce the run-time. In many cases the computation time required for a given calculation cannot be predetermined even at run-time and so static partition of the problem returns poor performance. For problems in which the computational load across the discretisation is dynamic and inhomogeneous, for example multi-physics problems involving fluid and solid mechanics with phase changes, the workload for a static subdomain will change over the course of a computation and cannot be estimated beforehand. For such applications the mapping of loads to process is required to change dynamically, at run-time in order to maintain reasonable efficiency. The issue of dynamic load balancing are examined in the context of PHYSICA, a three dimensional unstructured mesh multi-physics continuum mechanics computational modelling code.

Enhancing the numerical performance of fire field models (CMS Paper)

This paper describes two new techniques designed to enhance the performance of fire field modelling software. The two techniques are "group solvers" and automated dynamic control of the solution process, both of which are currently under development within the SMARTFIRE Computational Fluid Dynamics environment. The "group solver" is a derivation of common solver techniques used to obtain numerical solutions to the algebraic equations associated with fire field modelling. The purpose of "group solvers" is to reduce the computational overheads associated with traditional numerical solvers typically used in fire field modelling applications. In an example, discussed in this paper, the group solver is shown to provide a 37% saving in computational time compared with a traditional solver. The second technique is the automated dynamic control of the solution process, which is achieved through the use of artificial intelligence techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxation using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate the potential for enhanced solution reliability due to obtaining acceptable convergence within each time step, unlike some of the comparison simulations.

A methodology for building energy modelling and calibration in warm climates

PEDRINI, Aldomar; WESTPHAL, F. S.; LAMBERT, R.. A methodology for building energy modelling and calibration in warm climates. Building And Environment, Australia, n. 37, p.903-912, 2002. Disponível em: . Acesso em: 04 out. 2010.

A contribution towards the performance and structural understanding of copper and nickel salts as ammonia stores

The ongoing depletion of fossil fuels and the severe consequences of the greenhouse effect make the development of alternative energy systems crucially important. While hydrogen is, in principle, a promising alternative, releasing nothing but energy and pure water. Hydrogen storage is complicated and no completely viable technique has been proposed so far. This work is concerned with the study of one potential alternative to pure hydrogen: ammonia, and more specifically its storage in solids. Ammonia, NH3, can be regarded as a chemical hydrogen carrier with the advantages of strongly reduced flammability and explosiveness as compared to hydrogen. Furthermore, ammine metal salts presented here as promising ammonia stores easily store up to 50 wt.-% ammonia, giving them a volumetric energy density comparable to natural gas. The model system NiX2–NH3 ( X = Cl, Br, I) is studied thoroughly with respect to ammine salt formation, thermal decomposition, air stability and structural effects. The system CuX2–NH3 ( X = Cl, Br) has an adverse thermal decomposition behaviour, making it impractical for use as an ammonia store. This system is, however, most interesting from a structural point of view and some work concerning the study of the structural behaviour of this system is presented. Finally, close chemical relatives to the metal ammine halides, the metal ammine nitrates are studied. They exhibit interesting anion arrangements, which is an impressive showcase for the combination of diffraction and spectroscopic information. The characterisation techniques in this thesis range from powder diffraction over single crystal diffraction, spectroscopy, computational modelling, thermal analyses to gravimetric uptake experiments. Further highlights are the structure solutions and refinements from powder data of (NH4)2[NiCl4(H2O)(NH3)] and Ni(NH3)2(NO3)2, the combination of crystallographic and chemical information for the elucidation of the (NH4)2[NiCl4(H2O)(NH3)] formation reaction and the growth of single crystals under ammonia flow, a technique allowing the first documented successful growth and single crystal diffraction measurement for [Cu(NH3)6]Cl2.

Modelling human behaviour and event detection from videos

Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. However, challenges still exist owing to several factors such as inter- and intra-class variations, cluttered backgrounds, occlusion, camera motion, scale, view and illumination changes. This research focuses on modelling human behaviour for action recognition in videos. The developed techniques are validated on large scale benchmark datasets and applied on real-world scenarios such as soccer videos. Three major contributions are made. The first contribution is in the area of proper choice of a feature representation for videos. This involved a study of state-of-the-art techniques for action recognition, feature extraction processing and dimensional reduction techniques so as to yield the best performance with optimal computational requirements. Secondly, temporal modelling of human behaviour is performed. This involved frequency analysis and temporal integration of local information in the video frames to yield a temporal feature vector. Current practices mostly average the frame information over an entire video and neglect the temporal order. Lastly, the proposed framework is applied and further adapted to real-world scenario such as soccer videos. A dataset consisting of video sequences depicting events of players falling is created from actual match data to this end and used to experimentally evaluate the proposed framework.

Modelling of an efficient dynamic smart solar photovoltaic power grid system

A smart solar photovoltaic grid system is an advent of innovation coherence of information and communications technology (ICT) with power systems control engineering via the internet [1]. This thesis designs and demonstrates a smart solar photovoltaic grid system that is selfhealing, environmental and consumer friendly, but also with the ability to accommodate other renewable sources of energy generation seamlessly, creating a healthy competitive energy industry and optimising energy assets efficiency. This thesis also presents the modelling of an efficient dynamic smart solar photovoltaic power grid system by exploring the maximum power point tracking efficiency, optimisation of the smart solar photovoltaic array through modelling and simulation to improve the quality of design for the solar photovoltaic module. In contrast, over the past decade quite promising results have been published in literature, most of which have not addressed the basis of the research questions in this thesis. The Levenberg-Marquardt and sparse based algorithms have proven to be very effective tools in helping to improve the quality of design for solar photovoltaic modules, minimising the possible relative errors in this thesis. Guided by theoretical and analytical reviews in literature, this research has carefully chosen the MatLab/Simulink software toolbox for modelling and simulation experiments performed on the static smart solar grid system. The auto-correlation coefficient results obtained from the modelling experiments give an accuracy of 99% with negligible mean square error (MSE), root mean square error (RMSE) and standard deviation. This thesis further explores the design and implementation of a robust real-time online solar photovoltaic monitoring system, establishing a comparative study of two solar photovoltaic tracking systems which provide remote access to the harvested energy data. This research made a landmark innovation in designing and implementing a unique approach for online remote access solar photovoltaic monitoring systems providing updated information of the energy produced by the solar photovoltaic module at the site location. In addressing the challenge of online solar photovoltaic monitoring systems, Darfon online data logger device has been systematically integrated into the design for a comparative study of the two solar photovoltaic tracking systems examined in this thesis. The site location for the comparative study of the solar photovoltaic tracking systems is at the National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. The overall comparative energy output efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic monitoring system as observed at the research location site is about 72% based on the total energy produced, estimated money saved and the amount of CO2 reduction achieved. Similarly, in comparing the total amount of energy produced by the two solar photovoltaic tracking systems, the overall daily generated energy for the month of July shows the effectiveness of the azimuthal-altitude tracking systems over the 450 stationary solar photovoltaic system. It was found that the azimuthal-altitude dual-axis tracking systems were about 68.43% efficient compared to the 450 stationary solar photovoltaic systems. Lastly, the overall comparative hourly energy efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic energy system was found to be 74.2% efficient. Results from this research are quite promising and significant in satisfying the purpose of the research objectives and questions posed in the thesis. The new algorithms introduced in this research and the statistical measures applied to the modelling and simulation of a smart static solar photovoltaic grid system performance outperformed other previous works in reviewed literature. Based on this new implementation design of the online data logging systems for solar photovoltaic monitoring, it is possible for the first time to have online on-site information of the energy produced remotely, fault identification and rectification, maintenance and recovery time deployed as fast as possible. The results presented in this research as Internet of things (IoT) on smart solar grid systems are likely to offer real-life experiences especially both to the existing body of knowledge and the future solar photovoltaic energy industry irrespective of the study site location for the comparative solar photovoltaic tracking systems. While the thesis has contributed to the smart solar photovoltaic grid system, it has also highlighted areas of further research and the need to investigate more on improving the choice and quality design for solar photovoltaic modules. Finally, it has also made recommendations for further research in the minimization of the absolute or relative errors in the quality and design of the smart static solar photovoltaic module.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.

Transition to Product Service Systems: methodology based on scenarios identification, modelling and evaluation

Part 4: Transition Towards Product-Service Systems

Modelling and simulating retail management practices: a first approach

Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK’s top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering “what-if” questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers’ satisfaction with service, based on their individual shopping experiences.

Marketing channel behaviour and performance

Thesis submitted to University of Manchester for the degree of Doctor of Philosophy in the Faculty of Business Administration.

Integration of a graph-based model indexer in commercial modelling tools

Softeam has over 20 years of experience providing UML-based modelling solutions, such as its Modelio modelling tool, and its Constellation enterprise model management and collaboration environment. Due to the increasing number and size of the models used by Softeam’s clients, Softeam joined the MONDO FP7 EU research project, which worked on solutions for these scalability challenges and produced the Hawk model indexer among other results. This paper presents the technical details and several case studies on the integration of Hawk into Softeam’s toolset. The first case study measured the performance of Hawk’s Modelio support using varying amounts of memory for the Neo4j backend. In another case study, Hawk was integrated into Constellation to provide scalable global querying of model repositories. Finally, the combination of Hawk and the Epsilon Generation Language was compared against Modelio for document generation: for the largest model, Hawk was two orders of magnitude faster.