Integration of Green Quality Function Deployment and Fuzzy Multi-Attribute Utility Theory-Based Cost Estimation for Environmentally Conscious Product Development

There are increasing global demands for environmental friendly products. Green Quality Function Deployment - III (GQFD - III) is an innovative tool aiding in the development of environmentally conscious products and processes. An improved version of GQFD - III, Green Quality Function Deployment - IV (GQFD - IV) has been developed in this study. Its improvement over GQFD - III is that the life cycle cost is estimated using the Fuzzy Multi-Attribute Utility Theory (FMAUT) method. FMAUT costing is an excellent cost estimation method at the early design stage in product development. It is more effective than other traditional methods because it does not require detailed data on manufacturing processes of the product and it can handle attributes with uncertainty and incompleteness in nature. In a case study, life cycle costs of coffeemakers were estimated with errors of less than 7% using this new cost estimation model. In GQFD - IV, with the considerations of quality, environment and cost, analytical hierarchy process (AHP) is used for product concept selection and is found to be effective

Adaptive Finite Element Methods for Variational Inequalities: Theory and Applications in Finance

We consider variational inequalities (VIs) in a bounded open domain Omega subset IR^d with a piecewise smooth obstacle constraint. To solve VIs, we formulate a fully-discrete adaptive algorithm by using the backward Euler method for time discretization and the continuous piecewise linear finite element method for space discretization. The outline of this thesis is the following. Firstly, we introduce the elliptic and parabolic variational inequalities in Hilbert spaces and briefly review general existence and uniqueness results. Then we focus on a simple but important example of VI, namely the obstacle problem. One interesting application of the obstacle problem is the American-type option pricing problem in finance. We review the classical model as well as some recent advances in option pricing. These models result in VIs with integro-differential operators. Secondly, we introduce two classical numerical methods in scientific computing: the finite element method for elliptic partial differential equations (PDEs) and the Euler method for ordinary different equations (ODEs). Then we combine these two methods to formulate a fully-discrete numerical scheme for VIs. With mild regularity assumptions, we prove optimal a priori convergence rate with respect to regularity of the solution for the proposed numerical method. Thirdly, we derive an a posteriori error estimator and show its reliability and efficiency. The error estimator is localized in the sense that the size of the elliptic residual is only relevant in the approximate noncontact region, and the approximability of the obstacle is only relevant in the approximate contact region. Based on this new a posteriori error estimator, we design a time-space adaptive algorithm and multigrid solvers for the resulting discrete problems. In the end, numerical results for $d=1,2$ show that the error estimator decays with the same rate as the actual error when the space meshsize and the time step tend to zero. Also, the error indicators capture the correct local behavior of the errors in both the contact and noncontact regions

Review of natural fibre-reinforced hybrid composites

Natural fibre-reinforced hybrid composites which contain one or more types of natural reinforcement are gaining increasing research interest. This paper presents a review of natural fibre-reinforced hybrid composites. Both thermoplastic and thermoset composites reinforced by hybrid/synthetic fibres or hybrid/hybrid fibres are reviewed. The properties of natural fibres, the properties and processing of composites are summarised

Reducing the dynamic errors of coordinate measuring machines

Coordinate measuring machines (CMMs) are already widely utilized as measuring tools in the modern manufacturing industry. Fast and accurate probing is the current trend for the next generation of CMMs. However, measuring velocity of CMM applications are limited by dynamic errors that occur in CMMs. In this paper, the dynamic errors of coordinate measuring machines are analyzed theoretically and experimentally. The limited stiffness of air bearings were found to cause dynamic errors due to the existence of Abbe's offsets. The characteristics of the air bearings used on CMMs were modeled by the finite element analysis (FEA). The load capacity and stiffness of the air bearings were computed. Using this model, the dynamic errors of the CMM were reduced through revising the air bearing design. To verify the effectiveness of this approach, the performance of the air bearings was tested both statically and dynamically. The results show that the dynamic errors can be significantly reduced

Assembly dimensional variation modelling and optimization for the resin transfer moulding process

The increasing demand for composite products to be affordable, net-shaped and efficiently assembled makes tight dimensional tolerance critical. Due to lack of accurate process models, resin transfer moulding (RTM) dimensional analysis and control are often performed using trial-and-error approaches based on engineers' experiences or previous production data. Such approaches are limited to specific geometries and materials and often fail to achieve the required dimensional accuracy in the final products. This paper presents an innovative study on the dimensional variation prediction and control for fibre reinforced polymeric matrix composites. A dimensional variation model was developed for process simulation based on thermal stress analysis and finite element analysis (FEA). This model was validated against experimental data, analytical solutions and data from the literature. Using the FEA-based dimensional variation model, the deformations of typical composite structures were studied, and a regression-based dimensional variation model was developed. By introducing the material modification coefficient, this comprehensive model can account for various fibre/resin types and stacking sequences. The regression based dimensional variation model can significantly reduce computation time by eliminating the complicated, time-consuming finite element meshing and material parameter defining process and providing a quick design guide for composite products with reduced dimensional variations. The structural tree method (STM) is proposed to compute the assembly deformation from the deformations of individual components as well as the deformation of general shape composite components. The STM enables rapid dimensional variation analysis/synthesis for complex composite assemblies when used along with the regression-based dimensional variation model. The work presented here provides a foundation to develop practical dimensional control techniques for composite products

Dimension variation prediction for composites with finite element analysis and regression modeling

This paper presents a new method for efficient prediction of dimension variations of polymer matrix fiber reinforced composites. A dimension variation model was developed based on thermal stress prediction with finite element analysis (FEA). This model was validated against experimental data, analytical solutions and the data from literature. Using the FEA-based dimension variation model, deformations of typical composite structures were studied and regression-based dimension variation models were developed. By introducing the material modification coefficient, this comprehensive model can account for various fiber/resin types and stacking sequences. The regression-based dimension variation model can significantly reduce computation time by eliminating the complicated, time-consuming finite element meshing, material parameter defining and evaluation solving process, which provides a quick design guide for composite products with reduced dimension variations. The structural tree method (STM) was developed to compute the assembly dimension variation from the deformations of individual components, as well as the deformation of general shape composite components. The STM enables rapid dimension variation analysis/synthesis for complex composite assemblies with the regression-based dimension variation models. The exploring work presented in this research provides a foundation to develop practical and proactive dimension control techniques for composite products

Directed evolution of and structural insights into antibody-mediated disruption of a stable receptor-ligand complex.

Antibody drugs exert therapeutic effects via a range of mechanisms, including competitive inhibition, allosteric modulation, and immune effector mechanisms. Facilitated dissociation is an additional mechanism where antibody-mediated "disruption" of stable high-affinity macromolecular complexes can potentially enhance therapeutic efficacy. However, this mechanism is not well understood or utilized therapeutically. Here, we investigate and engineer the weak disruptive activity of an existing therapeutic antibody, omalizumab, which targets IgE antibodies to block the allergic response. We develop a yeast display approach to select for and engineer antibody disruptive efficiency and generate potent omalizumab variants that dissociate receptor-bound IgE. We determine a low resolution cryo-EM structure of a transient disruption intermediate containing the IgE-Fc, its partially dissociated receptor and an antibody inhibitor. Our results provide a conceptual framework for engineering disruptive inhibitors for other targets, insights into the failure in clinical trials of the previous high affinity omalizumab HAE variant and anti-IgE antibodies that safely and rapidly disarm allergic effector cells