In recent years, deep learning technologies have received huge attention and interest in the field of high-performance material design. This is primarily because deep learning algorithms in nature have huge advantages over the conventional machine learning models in processing massive amounts of unstructured data with high performance. Besides, deep learning models are capable of recognizing the hidden patterns among unstructured data in an automatic fashion without relying on excessive human domain knowledge. Nevertheless, constructing a robust deep learning model for assessing materials' structure-property relationships remains a non-trivial task due to highly flexible model architecture and the challenge of selecting appropriate material representation methods. In this regard, we develop advanced deep-learning models and implement them for predicting the quantum-chemical calculated properties (i.e., formation energy) for an enormous number of crystal systems. Chapter 1 briefly introduces some fundamental theory of deep learning models (i.e., CNN, GNN) and advanced analysis methods (i.e., saliency map). In Chapter 2, the convolutional neural network (CNN) model is established to find the correlation between the physically intuitive partial electronic density of state (PDOS) and the formation energies of crystals. Importantly, advanced machine learning analysis methods (i.e., salience mapping analysis) are utilized to shed light on underlying physical factors governing the energy properties. In Chapter 3, we introduce the methodology of implementing the cutting-edge graph neural networks (GNN) models for learning an enormous number of crystal structures for the desired properties. / Master of Science / Machine learning technologies, particularly deep learning, have demonstrated remarkable progress in facilitating the high-throughput materials discovery process. In essence, machine learning algorithms have the ability to uncover the hidden patterns of data and make appropriate decisions without being explicitly programmed. Nevertheless, implementing machine learning models in the field of material design remains a challenging task. One of the biggest limitations is our insufficient knowledge about the structure-property relationships for material systems. As the performance of machine learning models is to a large degree determined by the underlying material representation method, which typically requires the experts to have in-depth knowledge of the material systems. Thus, designing effective feature representation methods is always the most crucial aspect for machine learning model development and the process takes a significant amount of manual effort. Even though tremendous efforts have been made in recent years, the research process for robust feature representation methods is still slow. In this regard, we attempt to automate the feature engineering process with the assistance of advanced deep learning algorithms. Unlike the conventional machine learning models, our deep learning models (i.e., convolutional neural networks, graph neural networks) are capable of processing massive amounts of structured data such as spectrum and crystal graphs. Specifically, the deep learning models are explicitly designed to learn the hidden latent variables that are contained in crystal structures in an automatic fashion and provide accurate prediction results. We believe the deep learning models have huge potential to simplify the machine learning modeling process and facilitate the discovery of promising functional materials.

The sport and leisure industry in New Zealand (NZ) has the potential to become a major user of composite materials. Given the size of NZ industry, design and manufacturing strategies based on virtual engineering should be developed to suit NZ requirements. Virtual methods use computer aided engineering capabilities to find faults, explore alternatives and optimise product performance before detailed design or prototyping. When doing computer aided simulation the required mechanical properties of individual reinforcement and matrix components are well documented. However, the mechanical properties of composite materials are not as simple to obtain. Micro-mechanical modelling could therefore be used to aid the design and development of composite equipment, where mechanical properties are unknown. In this study, solids modelling was used to produce an analog model of a composite, and it was found that it lead to reductions in file size and simulation time. Representing a composite with an analog model implies that the behavioural characteristics are modelled, but not the physical characteristics of the individual components. Three micro-mechanical models were developed to predict the flexural modulus of composite materials, based on perfect, partial and no adhesion. It was found that the partial adhesion model was both practical and consistently accurate. The partial adhesion model accounted for adhesion between components by considering an 'effective shear value' at the interface. Validation of the models was done by flexural testing injection moulded samples of glass, wood and carbon fibre reinforced polyethylene. It was shown that the adhesion coefficient range was 0.1 for carbon fibre, 0.5 for glass fibre and 0.9 for the wood fibre composites. It was concluded that the adhesion coefficient is crucial and it is recommended that further work is done to validate effective shear values by empirical means. The predicted flexural modulus values were used to enable finite element simulation of modelled analog beams as well as commercial kayak paddles. It was determined that accurate simulation is possible for composite equipment using the partial adhesion model.

Due to the increasing demand of application-specific and/or multi-functional materials, it is necessary to integrate material design and product design. To support such design integration, this paper proposes a methodology to establish the relationships of both material design variables and product design variables. These variables include the required system performances and/or other evaluation criteria, and the relevant system loadings and attributes, where the attributes include both the product structural attributes and the material properties. This is achieved by modeling the behaviors of the product and those of the used material, and identifying the dependencies between the relevant design variables from the behaviors. The variable relationships can then be used to solve various design problems, such as design evaluation, evaluation and optimization of critical design variables, and so on. A design case study is also conducted to illustrate the proposed methodology and its usefulness. / Singapore-MIT Alliance (SMA)

Page builders är insticksmoduler till Wordpress, dessa blir ett allt mer populärt verktyg inom webbutveckling. De används idag av både frilansande designers och webbyråer som vill öka sin kostnadseffektivitet, men även av sådana som inte har någon erfarenhet alls inom webbutveckling. Syftet med denna typ av insticksmoduler är att förse användaren med de verktyg som behövs för att grafiskt skapa en webbsida. Detta examensarbete tittar närmare på de begränsningar Visual Composer medför i kombination med designmönstret Material Design och ramverket Materialize. Uppsatsen visar att Visual Composer kan vara ett bra verktyg för snabb prototyputveckling och för projekt med färre krav och designregler samt om det begränsade potential Visual Composers erbjuder även ses som ett eget designmönster redan tidigt i utvecklingen. / Page builders are becoming an increasingly popular tool in web development and are plugins for WordPress. Today they are used by both freelance designers and web agencies who want to increase their cost-effectiveness, but also by those who have no experience at all in web development. The purpose of this type of plugins is to provide the user with the tools needed to graphically create a web page. This practical thesis looks at the limitations Visual Composer cause in combination with Material Design principles and the framework Materialize. The thesis shows that Visual Composer can be a good tool for rapid prototyping and for projects with fewer requirements and design regulations and if the limited potential of Visual Composer is also seen as a design principle early on in development.

Composites have now revolutionized most industries, like aerospace, marine, electrical, transportation, and have proved to be a worthy alternative to other traditional materials. However for a further comprehensive usage, the tailorability of hybrid composites according to the specific application needs on a large-scale production basis is required. In this regard, one of the major fundamental research fields here involves a technology development based on the multiaxial warp-knitting technique for the production of bionic-inspired and application-specific textile preforms that are force compliant and exhibit multi-material design. This article presents a newly developed yarn (warp) path manipulation unit for multiaxial warp-knitting machines that enables a targeted production of customized textile preforms with the above characteristics. The technological development cycle and their experimental validation to demonstrate the feasibility of new technology through production of some patterns for different field of applications are then discussed.

In this dissertation, algorithms for creating estimated potentials for metals and modeling of nano composites are developed. The efficacy of the algorithms for estimated potentials were examined. The algorithm was found to allow molecular dynamic and Monte Carlo modeling to be included in the potential building process. Additionally, the spline based equations caused issues with the elastic constants and Young’s modulus due to extra local minima. Two algorithms were developed for improved modeling of nano composites: one was a random number generation algorithm for initializing polymer, second was a bonding algorithm for controlling bonds between polymer and nano particle. Both algorithms were effective in their tasks. Additionally, the algorithms for improved nano composite modeling were used for preliminary material design of PMMA metal oxide nano composite systems. The results from the molecular dynamic simulations show the bonding between polymer matrix and nanoparticle has a large effect on the Young’s modulus and if this bonding could be controlled, the tensile properties of PMMA-metal oxide nano composites could be tailored to the applications’ requirements. The simulations also showed bonding had caused changes in the density of the material which than effected the energy on the polymer chain and the Young’s modulus. A model was than developed showing the relationship between density and the chain energy, and density and the Young’s modulus. This model can be used for a better understanding and further improvement of PMMA-metal oxide nano composites.

This thesis project is a stand-alone project with the goal to develop an optimized material suited for speaker cabinets, with the focus on acoustic abilities, production possibilities and environmental impact. And to further on design a high performance to price speaker, using the developed material properties and todays technology. The thesis is focused heavily on testing material, starting with research and thereafter creating and testing samples, to continue with find a material combination that would work for a product fit for the market. The final product should fulfill the demands of typical furniture handling, meaning it should be able to be moved around and withstand moderate abuse from daily events. The project results in a functional prototype for evaluation of material and the overall design. The project is based on design methods and design thinking.

Introducing Plaster is a degree work in textile design exploring the fusion of natural dyes and plaster, and how this can be applied as a textile design material. The outcome is presented as an experimental investigation, placed in the context of surface and material design. This study derived from a growing interest in how new materials can be implemented into the field of textile design using established textile techniques and methods. Natural dye, texture and flexibility where explored through the method of hands-on-experimentation. The study moved foreword by asking the question: “What happens if?”, and the findings have been analyzed and selected for further development. The final collection consists of five pieces made entirely from plaster, showing another approach to how textile techniques and methods can be developed and adapted to fit materials from another field, for instance: Plaster.

The aim of this thesis is to create a mobile application for Android that will connect small wineries with their customers. The result is two applications, the first of which is for wineries and the second one for customers, who can search wineries by simple criteria, save them as favorites, make notes and much more. The applications respects Material Design rules. Google Firebase is used for work with data and Firebase Cloud Messaging handles notifications between devices.

In the computer graphics production environment, artists often must tweak specific lighting and material parameters to match a mind's eye vision of the appearance of a 3D scene. However, the interaction between a material and a lighting environment is often too complex to cognitively predict without visualization. Therefore, artists operate in a design cycle, where they tweak the parameters, wait for a visualization, and repeat, seeking to obtain a desired look. We propose the use of appearance-driven material design. Here, artists directly design the appearance of reflected light for a specific view, surface point, and time. In this thesis, we discuss several methods for appearance-driven design with homogeneous materials, spatially-varying materials, and appearance-matching materials, where each uses a unique modeling and optimization paradigm. Moreover, we present a novel treatment of the illumination integral using sampling theory that can utilize the computational power of the graphics processing unit (GPU) to provide real-time visualization of the appearance of various materials illuminated by complex environment lighting. As a system, the modeling, optimization and rendering steps all operate on arbitrary geometry and in detailed lighting environments, while still providing instant feedback to the designer. Thus, our approach allows materials to play an active role in the process of set design and story-telling, a capability that was, until now, difficult to achieve due to the unavailability of interactive tools appropriate for artists.