Intelligent conceptual mould layout design system (ICMLDS) : innovation report

Family Mould Cavity Runner Layout Design (FMCRLD) is the most demanding and critical task in the early Conceptual Mould Layout Design (CMLD) phase. Traditional experience-dependent manual FCMRLD workflow results in long design lead time, non-optimum designs and costs of errors. However, no previous research, existing commercial software packages or patented technologies can support FMCRLD automation and optimisation. The nature of FMCRLD is non-repetitive and generative. The complexity of FMCRLD optimisation involves solving a complex two-level combinatorial layout design optimisation problem. This research first developed the Intelligent Conceptual Mould Layout Design System (ICMLDS) prototype based on the innovative nature-inspired evolutionary FCMRLD approach for FMCRLD automation and optimisation using Genetic Algorithm (GA) and Shape Grammar (SG). The ICMLDS prototype has been proven to be a powerful intelligent design tool as well as an interactive design-training tool that can encourage and accelerate mould designers’ design alternative exploration, exploitation and optimisation for better design in less time. This previously unavailable capability enables the supporting company not only to innovate the existing traditional mould making business but also to explore new business opportunities in the high-value low-volume market (such as telecommunication, consumer electronic and medical devices) of high precision injection moulding parts. On the other hand, the innovation of this research also provides a deeper insight into the art of evolutionary design and expands research opportunities in the evolutionary design approach into a wide variety of new application areas including hot runner layout design, ejector layout design, cooling layout design and architectural space layout design

Genetic Programming to Optimise 3D Trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTrajectory optimisation is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on non-intersection with any obstacles as well as predefined performance metrics. In the context of UAVs, the goal is to minimise the cost of the route, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques including evolutionary computation have been applied to trajectory optimisation with various degrees of success. This thesis explores the use of genetic programming (GP) to optimise trajectories in 3D space, by encoding 3D geographic trajectories as syntax trees representing a curve. A comprehensive review of the relevant literature is presented, covering the theory and techniques of GP, as well as the principles and challenges of 3D trajectory optimisation. The main contribution of this work is the development and implementation of a novel GP algorithm using function trees to encode 3D geographical trajectories. The trajectories are validated and evaluated using a realworld dataset and multiple objectives. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness. Finally, insights and recommendations for future research in this area are provided, highlighting the potential for GP to be applied to other complex optimisation problems in engineering and science

Many-Objective Genetic Type-2 Fuzzy Logic Based Workforce Optimisation Strategies for Large Scale Organisational Design

Workforce optimisation aims to maximise the productivity of a workforce and is a crucial practice for large organisations. The more effective these workforce optimisation strategies are, the better placed the organisation is to meet their objectives. Usually, the focus of workforce optimisation is scheduling, routing and planning. These strategies are particularly relevant to organisations with large mobile workforces, such as utility companies. There has been much research focused on these areas. One aspect of workforce optimisation that gets overlooked is organisational design. Organisational design aims to maximise the potential utilisation of all resources while minimising costs. If done correctly, other systems (scheduling, routing and planning) will be more effective. This thesis looks at organisational design, from geographical structures and team structures to skilling and resource management. A many-objective optimisation system to tackle large-scale optimisation problems will be presented. The system will employ interval type-2 fuzzy logic to handle the uncertainties with the real-world data, such as travel times and task completion times. The proposed system was developed with data from British Telecom (BT) and was deployed within the organisation. The techniques presented at the end of this thesis led to a very significant improvement over the standard NSGA-II algorithm by 31.07% with a P-Value of 1.86-10. The system has delivered an increase in productivity in BT of 0.5%, saving an estimated £1million a year, cut fuel consumption by 2.9%, resulting in an additional saving of over £200k a year. Due to less fuel consumption Carbon Dioxide (CO2) emissions have been reduced by 2,500 metric tonnes. Furthermore, a report by the United Kingdom’s (UK’s) Department of Transport found that for every billion vehicle miles travelled, there were 15,409 serious injuries or deaths. The system saved an estimated 7.7 million miles, equating to preventing more than 115 serious casualties and fatalities

Digital design optimisation: new methods and tools for design and manufacturing of architectural objects

Tese de doutoramento em Arquitetura e TecnologiaThe integration in the design process of high-level objectives, such as those requirements directly related to functional performance and lightness or the efficient use of materials, is especially difficult and can no longer be directly and intuitively perceived by the designer. This is particularly true in the conceptual phase of a design process. Methods and design tools that take in account personal preference and cultural acceptance, and can combine the interactive behavior inherent to conceptual designing with the formal rigor of optimisation are therefore desirable. The aim of the work presented in this dissertation is to develop and test a method that could be employed to model and manage the design process individually, adapted to a particular design problem and along with the personal preferences of the designer. The method is part of a digital design process where simulation and analysis does not only support the project development process, but interacts and contributes with novel proposals, promotes a more creative exploration of the solution space and aims to integrate computational models in the reasoning process and the activities of the designer during the complete design process A digital design process allows for integration of simulation and analysis software right in the beginning of the project, but it will be the task of the designer to build his own software construct combining all necessary software programs with the intent to introduce optimisation efficiently and goal orientated into the design process. In this thesis a design method is presented based on a software construct combines parametric design with evolutionary principles with the intent to maximise explorative search in an iterative design process. This application consists of a loose combination of commercially available software programs and property scripts united towards a common goal. vi The ability of this method to interactively assist the designer during the design process is demonstrated and applied to the conceptual design of several case studies, in the form of shading devices. It is concluded that optimisation can be introduced at the very beginning of the process of designing and optimisation reveals to be helpful and increasingly needed for an effective design process when constraints and boundary conditions cannot be easily evaluated by a conventional intuitive process and general domain knowledge. This advance of performative orientated design can only be viable within a within a digital supported methodological approach.A integração no processo de design de objetivos meta projeto, tais como os requerimentos relacionados com a leveza, o desempenho funcional, ou o uso eficiente de materiais, são especialmente difíceis de compreender e já não podem ser entendidos direta e intuitivamente pelo próprio designer. Isto revela-se particularmente na fase conceptual do processo de desenvolvimento. Métodos e ferramentas de design que conseguem integrar a preferência pessoal do designer, fatores culturais e que podem ao mesmo tempo combinar um comportamento interativo - inerente ao design conceptual - com o rigor formal da otimização são, portanto, desejáveis. O objetivo do trabalho apresentado nesta dissertação é desenvolver e testar um método que pode ser modelado e organizado individualmente conforme o próprio processo de desenvolvimento de um designer. Este método pode adaptar-se a qualquer problema de design em particular e pode ser totalmente construído conforme as preferências pessoais e as necessidades do designer. O método é parte de um processo de design digital, onde a análise e a simulação não apenas apoiam o processo de desenvolvimento do projeto, mas também interagem como processo de exploração e contribuem com propostas para soluções diferentes. Pode desta forma contribuir para uma exploração mais completa e mais criativa do espaço de soluções. Esta abordagem quase completamente digital promove, ao mesmo tempo, a integração de modelos computacionais no raciocínio e nas atividades do designer durante o processo de design completo. Um processo de design digital permite a integração de software de simulação e de análise logo no início do projeto. No entanto, deverá ser a tarefa do designer construir o seu próprio software, combinando os programas e os scripts necessários com a intenção de introduzir a otimização no seu processo de design de uma maneira eficaz e eficiente e, claro, em conformidade com o objetivo específico do processo. Nesta tese, é apresentado um método baseado na combinação de softwares, que juntamente com uma geometria paramétrica e com princípios evolutivos, permitam maximizar a pesquisa exploratória do espaço das soluções num processo de design iterativo. Este método consiste numa combinação, com um objectivo comum, de programas de software disponíveis no mercado e de scripts desenvolvidos pelo próprio designer. A capacidade deste método apoiar o designer, de uma forma interativa, durante a fase conceptual do processo de design é demonstrada e aplicada a um projeto conceptual de sombreadores. Concluiu-se que a otimização pode ser introduzida durante a fase conceptual do processo de design e que a otimização se revela não só útil, mas cada vez mais necessária para um desenvolvimento eficaz, quando os constrangimentos e os limites não podem ser facilmente avaliados por um processo intuitivo e convencional baseado no conhecimento geral do domínio. O avanço do design orientado para a performance só é possível dentro de uma abordagem metodológica digital

Robust and Optimal Methods for Geometric Sensor Data Alignment

Geometric sensor data alignment - the problem of finding the rigid transformation that correctly aligns two sets of sensor data without prior knowledge of how the data correspond - is a fundamental task in computer vision and robotics. It is inconvenient then that outliers and non-convexity are inherent to the problem and present significant challenges for alignment algorithms. Outliers are highly prevalent in sets of sensor data, particularly when the sets overlap incompletely. Despite this, many alignment objective functions are not robust to outliers, leading to erroneous alignments. In addition, alignment problems are highly non-convex, a property arising from the objective function and the transformation. While finding a local optimum may not be difficult, finding the global optimum is a hard optimisation problem. These key challenges have not been fully and jointly resolved in the existing literature, and so there is a need for robust and optimal solutions to alignment problems. Hence the objective of this thesis is to develop tractable algorithms for geometric sensor data alignment that are robust to outliers and not susceptible to spurious local optima. This thesis makes several significant contributions to the geometric alignment literature, founded on new insights into robust alignment and the geometry of transformations. Firstly, a novel discriminative sensor data representation is proposed that has better viewpoint invariance than generative models and is time and memory efficient without sacrificing model fidelity. Secondly, a novel local optimisation algorithm is developed for nD-nD geometric alignment under a robust distance measure. It manifests a wider region of convergence and a greater robustness to outliers and sampling artefacts than other local optimisation algorithms. Thirdly, the first optimal solution for 3D-3D geometric alignment with an inherently robust objective function is proposed. It outperforms other geometric alignment algorithms on challenging datasets due to its guaranteed optimality and outlier robustness, and has an efficient parallel implementation. Fourthly, the first optimal solution for 2D-3D geometric alignment with an inherently robust objective function is proposed. It outperforms existing approaches on challenging datasets, reliably finding the global optimum, and has an efficient parallel implementation. Finally, another optimal solution is developed for 2D-3D geometric alignment, using a robust surface alignment measure. Ultimately, robust and optimal methods, such as those in this thesis, are necessary to reliably find accurate solutions to geometric sensor data alignment problems

Optical properties of metal nanoparticles and their influence on silicon solar cells

The optical properties of metal nanoparticles have been investigated by simulation and experimental techniques. The aim of this investigation was to identify how to use metal nanoparticles to improve light-trapping in silicon solar cells. To do this we require nanoparticles that exhibit a high scattering efficiency and low absorption (i.e. high radiative efficiency) at near-infrared wavelengths. The simulation results identified Ag, Au, Cu and Al as potential candidates for use with silicon solar cells. The optical properties of Ag, Au and Cu nanoparticles are very similar above 700 nm. Below this wavelength Ag was found to be the preferred choice due to a decreased effect from interband transitions in comparison with Au and Cu. Al nanoparticles were found to exhibit markedly different optical properties to identical noble metal nanoparticles, with broader, weaker resonances that can be excited further into the UV. However, Al nanoparticles were found to exhibit higher absorption than noble metals in the NIR due to a weak interband region centred at around 825 nm.Tuning of the resonance position into the NIR was demonstrated by many methods, and extinction peaks exceeding 1200 nm can be achieved by all of the metals studied. However, it is important that the method used to red-shift the extinction peak does not also decrease the radiative efficiency. Core-shell nanoparticles, triangular nanoparticles and platelet-type nanoparticles were found to be unsuitable for silicon solar cells applications due their low radiative efficiencies. Instead, we propose the use of large (> 150 nm) Ag spheroids with moderate aspect ratios. A maximum radiative efficiency of 0.98 was found for noble metal nanospheres when the diameter exceeded 150 nm.The optical properties of Au and Al nanoparticles fabricated by electron-beam lithography were found to be in good agreement with simulations, provided that the substrate and local dielectric environment were accounted for by inclusion of an effective medium in the model. Cr adhesion layers were found to substantially weaken the extinction peaks of Au nanoparticles, and also result in a strong decrease of radiative efficiency. Adhesion layers were not required for Al nanoparticles. The morphological and optical properties of Ag island films were found to be highly dependent on the layer thickness, deposition speed and anneal temperature. Dense arrays containing average particle sizes ranging from 25 nm to 250 nm were achieved using anneal temperatures lower than 200oC. The largest nanoparticles were found to exhibit high extinction from 400 nm to 800 nm.Depositing Ag nanoparticles onto a-Si:H solar cells was found two have two effects on the spectral response. At short wavelengths the QE was decreased due to absorption by small particles or back-scattering by larger particles. At longer wavelengths large maxima and minima are present in the QE spectra. This latter effect is not due to excitation of surface plasmons, but is instead related to modification of interference effects in the thin-film layer stack

Multi-objective optimisation methods applied to complex engineering systems

This research proposes, implements and analyses a novel framework for multiobjective optimisation through evolutionary computing aimed at, but not restricted to, real-world problems in the engineering design domain. Evolutionary algorithms have been used to tackle a variety of non-linear multiobjective optimisation problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the number of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising evolutionary algorithm framework, incorporating a genetic algorithm, that uses self-adaptive mutation and crossover in an attempt to avoid such problems, and which has been benchmarked against both standard optimisation test problems in the literature and a real-world airfoil optimisation case. For this last case, the minimisation of drag and maximisation of lift coefficients of a well documented standard airfoil, the framework is integrated with a freeform deformation tool to manage the changes to the section geometry, and XFoil, a tool which evaluates the airfoil in terms of its aerodynamic efficiency. The performance of the framework on this problem is compared with those of two other heuristic MOO algorithms known to perform well, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this framework achieves better or at least no worse convergence. The framework of this research is then considered as a candidate for smart (electricity) grid optimisation. Power networks can be improved in both technical and economical terms by the inclusion of distributed generation which may include renewable energy sources. The essential problem in national power networks is that of power flow and in particular, optimal power flow calculations of alternating (or possibly, direct) current. The aims of this work are to propose and investigate a method to assist in the determination of the composition of optimal or high-performing power networks in terms of the type, number and location of the distributed generators, and to analyse the multi-dimensional results of the evolutionary computation component in order to reveal relationships between the network design vector elements and to identify possible further methods of improving models in future work. The results indicate that the method used is a feasible one for the achievement of these goals, and also for determining optimal flow capacities of transmission lines connecting the bus bars in the network

Meta-parametric design: Developing a computational approach for early stage collaborative practice

Computational design is the study of how programmable computers can be integrated into the process of design. It is not simply the use of pre-compiled computer aided design software that aims to replicate the drawing board, but rather the development of computer algorithms as an integral part of the design process. Programmable machines have begun to challenge traditional modes of thinking in architecture and engineering, placing further emphasis on process ahead of the final result. Just as Darwin and Wallace had to think beyond form and inquire into the development of biological organisms to understand evolution, so computational methods enable us to rethink how we approach the design process itself. The subject is broad and multidisciplinary, with influences from design, computer science, mathematics, biology and engineering. This thesis begins similarly wide in its scope, addressing both the technological aspects of computational design and its application on several case study projects in professional practice. By learning through participant observation in combination with secondary research, it is found that design teams can be most effective at the early stage of projects by engaging with the additional complexity this entails. At this concept stage, computational tools such as parametric models are found to have insufficient flexibility for wide design exploration. In response, an approach called Meta-Parametric Design is proposed, inspired by developments in genetic programming (GP). By moving to a higher level of abstraction as computational designers, a Meta-Parametric approach is able to adapt to changing constraints and requirements whilst maintaining an explicit record of process for collaborative working

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario