Laser powder bed fusion offers many advantages over conventional manufacturing methods, such as the integration of multiple parts which can result in significant weight-savings. The increased design freedom that layer-wise manufacture allows has also been seen to enhance component performance at little or no added cost. However, for such benefits to be realised, the material quality must first be assured. Laser ultrasonic testing is a non-contact inspection technique which has been proposed as suitable for in-situ monitoring of metal additive manufacturing processes. The thesis presented here explores the current capability of this technique to detect manufactured, seeded and process generated sub-surface “defects” in Ti6Al4V samples, ex-situ. The results are compared with X-ray computed tomography reconstructions, focus variation microscopy and destructive testing. Whilst laser ultrasound has been used to successfully identify a range of material discontinuities, further work is required before this technique could be implemented in-situ.

The continuous increase of Turbine Entry Temperature (TET) in aerospace gas-turbines is the main driver for the research effort in the development of coatings for thermal and oxidation protection: i.e. Thermal Barrier Coatings (TBC). The need for TBC is particularly relevant within the combustor assembly (or simply combustion chamber) where the highest temperatures within a gas turbine, in excess of 1700 C, are registered. Due to the harsh thermal and oxidative conditions experienced within the combustion chamber, TBC are subjected to several degradation mechanisms which generally result in spallation (or delamination) of the coating. Spallation is more likely to be observed at specific locations within a combustion chamber, and acceptance limits for this quantity are specified by the Original Equipment Manufacturer (OEM). To reduce the risk that coating spallation will lead to an unplanned engine removal, in-situ coating re-deposition may be possible. However, the development of such a technology poses significant challenges. The selected deposition process must, in fact, be able to operate in a confined environment (i.e. the combustion chamber) and produce TBC of microstructure providing adequate thermal and oxidation protection properties. Moreover, a deep understanding between the process parameters and both microstructure and shape of produced coating is necessary to achieve an optimum control over the whole deposition process. Therefore, after an initial selection of Combustion Flame Spray (CFS) as TBC deposition technology, the present thesis has the following objectives: (i) analysing in-depth the physics/chemistry of coating build-up at a microscopic level (i.e. single-splat) in order to relate this to fundamental properties (e.g. adhesion and residual stress) measured at macroscopic coating level, (ii) investigating the relationship between process parameters and their effect on the material properties of the deposit, in order to determine an optimum process parameters "window" and (iii) to develop a mathematical framework that accounts for the stochastic nature of the deposition process, and has the capability to predict the deposit growth geometry with high spatial accuracy for different process parameters. For coating build-up analysis purposes, a novel set of experimental tools is developed, allowing to model fundamental flattening and solidification mechanisms with a sub-micrometre spatial resolution. For deposition parameters optimisation purposes, an extensive experimental analysis of the effect of deposition parameters including: powder morphology (size and shape), equivalence ratio, powder feed rate, carrier gas flow and torch-to-substrate standoff distance has been performed for the CFS-produced TBC to complete the lack of knowledge in literature data. Finally, the deposit growth model allows to predict, in the time domain, the three-dimensional footprint (i.e. deposit shape) and temperature of CFS and generally thermal spray deposits. For this purpose, a three-dimensional implicit finite-difference algorithm, based on two interplaying geometrical and thermal-analysis sections, has been developed. The work of this thesis thus provides a step forward in the understanding of the thermal spray deposit formation process. In fact, the determined correlation between properties at both single splat and coating level represents a powerful tool making the optimisation of process parameters-coating properties relationship more efficient as opposed to traditional trial-and-error approaches. Moreover, the developed calibration-based deposit growth model results of simple application, opening the way for spray automation in difficult-to-spray geometries and/or repair applications for several thermal spray processes.

Drawbeads control the flow of material into the die cavity during pressing operations. The tribological and forming properties of aluminium necessitate specific frictional and drawbead geometry requirements that are different from those established over many years for steels. Academic research on this topic is limited, requiring industry to rely on trial and error methods to determine the coefficient of friction and drawbead geometry. This research project focused on developing an innovative, scientific and holistic methodology to determine the optimum drawbead geometry and an appropriate coefficient of friction value to be used in forming feasibility simulations for aluminium panels. Special attention was given to the ease with which this research could be implemented in an industrial environment. Hence, extensive experiments to gather material properties such as plane strain and pure shear tests, complex material models, or optimisation models based on artificial neural networks (ANN), and non-linear friction models were avoided. Three approaches identified in the literature for designing drawbeads, namely, experimental, analytical and numerical modelling were investigated to test the underlying assumptions, strengths and limits of each. For example, analytical models assumed symmetric material flow passing over the drawbeads, which in reality does not occur. Based on these findings a systematic, hybrid approach has been developed which uses a combination of physical drawbead tests and numerical modelling, to determine the coefficient of friction which is then used to obtain the drawbead restraining force. Using a novel criterion, different drawbead geometry conditions have been ranked to aid selection of an optimised drawbead geometry. The optimised drawbead geometry obtained from the hybrid approach was validated by stamping of rectangular pans. The rectangular pan, when stamped using the optimised geometry obtained from the hybrid approach, did not show defects such as severe thinning and wrinkles. The numerical stamping model with geometric drawbead predicted the punch force with a 4.5% error, thinning with a 5% error and draw-in with an 8% error. An innovative hybrid approach has been proposed which is capable of accurately predicting the coefficient of friction, the drawbead restraining force and the drawbead geometry. The same coefficient of friction and the drawbead geometry when used in the forming simulation accurately predicted the punch force, thinning and draw-in. As a direct application of innovation, Jaguar Land Rover can use the novel criteria for selecting the drawbead geometry to use effectively the drawbead geometry generation feature in the commercial sheet metal forming software package during forming feasibility simulations. The hybrid approach can potentially save 34% of the die tryout time and provide average cost savings of £34,400 per die set per tryout attempt.

The release of new products to the market goes through a process of Research and Development (R&D), manufacturing, and service respectively. Much research has been done on improving manufacturing processes. This research focuses on enhancing current Manufacturing Strategies by making three important and related groups of contributions within R&D and service. The aim of the research was to develop tools and techniques to increase the ‘Fitness” of a company. The first contribution relates to a Fit Development Toolbox (FDT) to identify the interdependencies between the 11 well-known tools of Toyota’s Product Development (PD) system within R&D. Using the proposed FDT a new framework has been developed for implementing Toyota’s PD system more efficiently based on the primary data collected from 112 companies. The second contribution deals with all sources of Unfit demand and how waste affects the flow of processes within R&D. This has been achieved by developing the Fit Flow Index (FFI) using primary data from 322 companies to measure the flow of the PD system at any stage of the project. The proposed index decreases the chances of creating queues and bottlenecks within the PD system which in turn enhances innovation and productivity. The third contribution concentrates on formulating a Fitness index for evaluating quality of services provided by Starbucks after adopting Lean principles. A Fit Customer Satisfaction (FCS) score was calculated using primary data collected from 822 surveys, which in conjunction with the proposed Fitness Index (FI), enables companies to measure their Fitness level in response to applying continuous improvements.

There is a general consensus that human activity has significant impact on global climate change with significant consequences to the environment. Although there has been relatively limited research on the relationship between corporate environmental performance and corporate financial performance, empirical of the relationship between proactive corporate climate change strategies and economic performance is still in need of clear delineation. It is in light of this hat this research examines the impacts of sustainability adoption on competitive performance of oil and gas companies. The research explores the notion of market driven sustainability by establishing an empirical link between sustainable supply chains characteristics and organisational competitiveness. The overall aim is to develop an empirical model of sustainable supply chain characteristics that improves resource utilisation, profit maximization and competitiveness in the oil and gas industry. The research reviews existing literature on supply chain management, sustainability and competitive objectives in order to generate an appropriate and adequate context for relevant analytical investigations. Primary data on sustainability and its impacts on organisational performance were collected from UK and gas industry through survey by questionnaire. The results show that the most significant drivers of sustainability are the desire to conserve energy, increase market share and improve competiveness. However, legal and regulatory pressure, in contrast to common perspectives in the literature, was not seen as strong drivers of sustainability. The most significant inhibitors of sustainability are inappropriate infrastructural facilities, higher take-up costs, shortage of information on sustainability and employees lack of environmental awareness. The results further indicate that, though sustainability strategies implemented by the respondent firms varied in scopes, these strategies were being extensively and successfully implemented. Generally, the adoption of sustainability in oil and gas supply chain leads to improved economic performance and environmental performance, which, in turn, positively impact organisational competitiveness. These results are of particular importance to managers, government policy makers environmentalists and researchers.

In the competitive world, time to market, new technology and innovation are the measures of the performance of New Product Development (NPD). Companies tend to use a conventional approach to NPD by assigning representatives from their own support functions to review and recommend changes as projects evolve. In recent years, this approach has been questioned since it is a costly and time-consuming approach due to its iterative nature. Researchers argue that the time to market process and the cost of NPD can be reduced considerably by involving the support functions of the supply chain to a greater extent and also earlier in the NPD process. There is a potential industrial requirement for a collaborative framework that facilitates the linkage between Supply Chain Management (SCM) and New Product Development (NPD). This research project focuses on the early stages of the collaborative product development process in the extended enterprise. The research output includes the functional requirements of a framework and a developed prototype methodology with tools and technologies that are tested from case studies within industry. The research also introduces the development and analysis of the framework that allows the integration of the flow of product development related activities within original equipment manufacturers (OEM) and suppliers providing future business benefits. An industrial investigation of an OEM in the automotive industry within the research identified that there are different decision making points in product development and manufacturing. The proposed methodology and framework use key drives to predict and quantify its impact on four main criteria namely: feasibility, time, cost and capability that support or advise on key decision making of OEM’s product development and management process.

Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption focuses on the need to improve the efficiency of resources (machines). The potential for energy reducing at the system-level has been largely ignored. At this level, operational research methods can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can be applied across existing legacy systems and does not require a large investment. For the emission reduction purpose, some electricity usage control policies and tariffs (EPTs) have been promulgated by many governments. The Rolling Blackout policy in China is one of the typical EPTs, which means the government electricity will be cut off several days in every week for a specific manufacturing enterprise. The application of the Rolling Blackout policy results in increasing the manufacturing enterprises’ costs since they choose to start to use much more expensive private electricity to maintain their production. Therefore, this thesis develops operational research methods for the minimisation of electricity consumption and the electricity cost of job shop type of manufacturing systems. The job shop is selected as the research environment for the following reasons. From the academic perspective, energy consumption and energy cost reduction have not been well investigated in the multi-objective scheduling approaches to a typical job shop type of manufacturing system. Most of the current energy-conscious scheduling research is focused on single machine, parallel machine and flow shop environments. From the practical perspective, job shops are widely used in the manufacturing industry, especially in the small and medium enterprises (SMEs). Thus, the innovative electricity-conscious scheduling techniques delivered in this research can provide for plant managers a new way to achieve cost reduction. In this thesis, mathematical models are proposed for two multi-objective job shop scheduling optimisation problems. One of the problems is a bi-objective problem with one objective to minimise the total electricity consumption and the other to minimise the total weighted tardiness (the ECT problem). The other problem is a tri-objective problem which considers reducing total electricity consumption, total electricity cost and total weighted tardiness in a job shop when the Rolling Blackout policy is applied (the EC2T problem). Meta-heuristics are developed to approximate the Pareto front for ECT job shop scheduling problem including NSGA-II and a new Multi-objective Genetic Algorithm (GAEJP) based on the NSGA-II. A new heuristic is proposed to adjust scheduling plans when the Rolling Blackout policy is applied, and to help to understand how the policy will influence the performance of existing scheduling plans. NSGA-II is applied to solve the EC2T problem. Six scenarios have been proposed to prove the effectiveness of the aforementioned algorithms. The performance of all the aforementioned heuristics have been tested on Fisher and Thompson 10×10, Lawrence 15×10, 20×10 and 15×15 job shop scenarios which were extended to incorporate electrical consumption profiles for the machine tools. Based on the tests and comparison experiments, it has been found that by applying NSGA-II, the total non-processing electricity consumption in a job shop can decrease considerably at the expense of the schedules’ performance on the total weighted tardiness objective when there are tight due dates for jobs. When the due dates become less tight, the sacrifice of the total weighted tardiness becomes much smaller. By comparing the Pareto fronts obtained by GAEJP and by NSGA-II, it can be observed that GAEJP is more effective in reducing the total non-processing electricity consumption than NSGA-II, while not necessarily sacrificing its performance on total weighted tardiness. Thus, the superiority of the GAEJP in solving the ECT problem has been demonstrated. The scheduling plan adjustment heuristic has been proved to be effective in reducing the total weighted tardiness when the Rolling Blackout policy is applied. Finally, NSGA-II is proved to be effective to generate compromised scheduling plans for using the private electricity. This can help to realise the trade-off between the total weighted tardiness and the total electricity cost. Finally, the effectiveness of GAJEP in reducing the total non-processing electricity consumption has been validated in a real-world job shop case.

This thesis relates to the area of short term scheduling and control in batch process plants. A batch process plant consists of individual plant items linked by a pipe network through which product is routed. The structure of the network and the valve arrangements which control the routing severely constrains the availability of plant items for configuration in routes when a plant is operating. Current approaches to short term scheduling contain simplifying assumptions which ignore these constraints and this leads to unrealistic and infeasible schedules. The work undertaken investigates the use of techniques from the areas of Artificial Intelligence (AI) and Discrete Event Simulation (DES) in order to overcome these simplifying assumptions and develop good schedules which can be implemented in a plant. The main divisions of work cover a number of areas. The development of a representation scheme for batch plant networks, and procedures for reasoning about the constraints imposed by their structure to infer the actual availability of plant items for routing purposes at any time. The development of a dynamic rule-based route configuration procedure which takes into account the constraints on plant item availability. The development of an activity scheduling framework for batch plants based on this. The development of a dynamic simulation model to take account of finite capacity constraints in a batch plant. The integration of these elements in a hybrid structure to make best use of the techniques available from the areas of AI and DES. The representation scheme and procedures developed for reasoning about the constraints in a plant network enable the simplifying assumptions of other approaches to be overcome so that the system can produce good feasible schedules. The hybrid structure is a practical one to take for implementation and enables the best use of techniques from AI and DES.

The performance of woven fabric in many technical applications, such as airbags or reinforced composites, relates to fabric through-thickness permeability. A unified analytical model for woven fabric through-thickness permeability is proposed. It involves flow through gaps between yarns and within the yarns in terms of fabric porosity. The yarn permeability is a combination of flow along and transverse to unidirectional fibres. It is a function of fibre radius, fibre volume fraction, fibre array and crimp angle of interwoven yarns. The gap permeability is developed based on viscous and incompressible Hagen-Poiseuille flow in the gaps at low R_e values. The gap is simplified as a smooth fluid channel at the centre with slowly varying circular cross-section. The shape of the channel is approximated by a parabolic function. Volumetric flow rate is formulated as a function of pressure drop and flow channel geometry for the gap. The gap permeability is calculated thereafter according to Darcy’s law. For a woven fabric subjected to a high pressure load, an energy-based model is developed to predict the fabric out-of-plane deformation using minimum energy theory and an isotropic assumption for woven fabric. The model can predict the fabric maximum displacement and corresponding deflected profile across a diameter given a pressure load. The fabric deflection can be used to obtain the fabric elongation (strain) which results in the change of gap size, yarn width, yarn shape and fabric thickness in loose fabric (clear gaps between yarns) and the change of fibre volume fraction and crimp angle in tight fabric (overlapping yarns). The deformed fabric permeability is calculated by the unified permeability model based on the assumptions of the variation of geometric factors with deformation. If a woven fabric is subjected to a high decreasing pressure drop by air discharge, the fabric permeability is obtained by fitting pressure history and corresponding flow velocity using the Forchheimer equation. A nonlinear relationship is found between the pressure and velocity where the corresponding permeability is also called the dynamic permeability. The high pressure causes the shape of flow streamlines to vary in the gap between yarns (viewed as a converging-diverging duct). This flow behaviour is modelled by adding a non-Darcy term to Darcy’s law according to continuity theory and the Bernoulli equation. Therefore, a predictive Forchheimer equation is given for flow behaviour in a woven fabric based on the fabric geometry, structure and flow situation. The developed analytical models were verified by CFD simulations and experiments in this thesis. The comparisons showed good agreements. Sensitivity studies were conducted to understand the effects of geometric factors and mechanical properties on the fabric deformation and permeability. In this thesis, two pieces of equipment in particular were introduced for measuring the fabric dynamic permeability and fabric out-of-plane deformation. The measurements agreed well with their corresponding analytical predictions. Finally, the comparison of fabric deformation and non-Darcy flow showed the importance of fabric deformation in affecting the final fabric permeability.

This thesis presents the development of a software package to simulate and graphically represent the tool/workpiece kinematics in rotary forging. A hypothetical workpiece of non-constant volume is always used. Share changes are achieved by a process of material removal, analogous to metal cutting. Using a quantitative approach, the software is shown to be capable of calculating the tool/workpiece instantaneous contact zone and the volume of material being removed. The software has been developed and used to produce an analysis of radially configurated conic tools. This is believed to be a unique approach to the simulation of the rotary forging process. Various types of rotary forging machines can be identified by the motion of the conic tool and the hypothetical workpiece. A mathematical/ geometrical model is developed which can be used to simulate all possible rotary forging die motions. The model is used to determine the position in space of any point on the die, regardless of its motion and geometry. The software development has been used to graphically simulate the loci of points on a die, during movement of the die. The mathematical model is used to simulate a non-constant volume workpiece consisting of a large number of concentric cylinders of specified height and radius, with infinitesimal thickness. The interaction between tool and workpiece is achieved by using a method of geometric comparison. This allows an assessment of changes in the shape of the workpiece. Extension of the program, using a generated mesh, results in a numerical analysis of the rotary forging process. The data generated from the simulation phase, incorporating some previously developed software, is used to calculate the instantaneous area of contact and the volume of material being removed. Radially configurated tooling is achieved by the introduction of a database, into the software package. Guidelines are established for the practical design of radially configurated tools. The ability of the program to interact radially configurated tools with non-constant volume workpieces, is graphically/numerically investigated. The developed program could offer many potential applications in areas such as: the calculation of forming loads and stresses, pressure distribution, etc. Further, the program can establish some basic boundary conditions; which are essential information for the development of any finite element package for predicting metal flow in rotary forging.