An engineering design methodology helps designers to design in a systematic way. Based on the findings from a literature review, engineering design methodologies can be categorised into three types: prescriptive, descriptive and normative. Most established design methodologies are of the prescriptive type and they are based on step-oriented models. However, designers in industry are not found to be too keen on using any of these design methodologies. Among the reasons for not adopting these methodologies are that the prescriptive and normative design methodologies were found to be influencing the design strategies and approaches of a designer while the descriptive types were mostly used to study the design process. Though designers have their own design strategies and approaches, they also need design support. The descriptive type will not interfere with the designer’s strategies but they do suffer from a lack of structure in supporting designers. The goal of this research is to derive a design methodology framework to support designers without influencing their design approaches and strategies. A descriptive design methodology framework to support designers is proposed in this research work. This framework was derived based on four aspects: a descriptive type based on a function-oriented model, the types of support facilities that can be provided, identification of critical design factors as design parameters for the framework and lastly, the adaptation of the Ishikawa fishbone diagram to represent the framework. The novel descriptive design methodology was applied in two case studies: the first with an experienced designer without using any design methods and second, with a novice designer adopting a design approach based on the step-oriented model. The second case study included an additional design tool based on TRIZ to verify the effectiveness of the novel descriptive design methodology working with other tools. The designers’ feedback and observations from these both case studies showed that the novel descriptive design methodology was able to support designers in many ways. In particular it was able to accommodate different design approaches and strategies without influencing the designer, providing both methodology-related and computational-platform related support facilities as well as working in a complementary way with other design tools.

This research is devoted to the engineering of a generic, reliable and cost-effective method for the investigation of accuracy in layer based fabrication technologies. It begins with a review of the causes of deviations and uncertainties in component parts, analyses of the existing approaches for accuracy investigation and their limitations and disadvantages. The main focus of the research is the development of an original and convenient methodology capable of defining the dimensional uncertainties and accuracy of the technologies and the distribution of dimensional errors within the entire build area. The Grid Methodology is based on the discretisation of the object to allow the measurement, calculation, visualisation and analysis of part distortion in terms of linear and shear deviations from nominal. A single test piece and routine measurement procedure are utilised to estimate the distribution of the above entities; calculated in a similar way to the geometrical characteristics of strains in solid mechanics. The methodology was applied to research the causes of inaccuracy in the vertical direction of SLS Polystyrene. The presence of a critical dimension in height from where the distortion changes from shrinkage to extension was revealed and explained. The methodology was also utilised to estimate the necessary scaling factors to improve part accuracy, based on the calculated distortions. Implementation of the Grid Method to Micro Projection Stereolithography resulted in the ability to describe and estimate curling distortion in terms of angular deviations from nominal and separate it from linear distortions. - ii - Furthermore the application of the GM to the emerging micro-nano manufacturing sector has been shown to support the assessment of process capability. This provides a means of calculating process tolerances using results obtained from the single test piece. Investigation of the accuracy capabilities of three micro-processes was performed and their compatibility for designing process chains presented.

Additive Manufacturing (AM), popularly called “3D printing,” has benefited from many two-dimensional (2D) printing technology developments, but has yet to fully exploit the potential of digital printing techniques. The very essence of AM is accurately forming individual layers and laminating them together. One of the best commercially proven methods for forming complex powder layers is laser printing, which has yet to be used to directly print three-dimensional (3D) objects above the microscale, despite significant endeavour. The core discovery of this PhD is that the electrostatic charge on toner particles, which enables the digital material patterning capabilities of 2D laser printing/photocopying, is disabling for building defect-free 3D objects after the manner attempted to date. Toner charge is not mostly neutralized with fusing as previously assumed. This work characterizes and substantiates the accumulation of residual toner charge as a primary cause for defects arising in 3D printed bodies. Next, various means are assessed to manage and neutralize residual toner charge. Finally, the complementary implementation of charge neutralization with electrostatic transfer methods is explored.

Due to current trends in adopting flexible manufacturing philosophies, there has been a growing interest in applying Artificial Intelligence (AI) techniques to implement these manufacturing strategies. This is because conventional computational methods alone are not sufficient to meet these requirements for more flexibility. This research examines the possibility of applying AI techniques to process planning and also addresses the various problems when implementing such techniques. In this project AI planning techniques were reviewed and some of these techniques were adopted and later extended to develop an assembly planner to illustrate the feasibility of applying AI techniques to process planning. The focus was on assembly process planning because little work in this area has been reported. Logical decisions like the sequencing of tasks which is a part of the process planning function can be viewed as an AI planning problem. The prototype Automatic Assembly Planner (AAP) was implemented using Edinburgh Prolog on a SUN workstation. Even though expected assembly sequences were obtained, the major problem facing this approach and perhaps AI applications in general is that of extracting relevant design data for the process planning function as illustrated by the planner. It is also believed that if process planning can be regarded as making logical decisions with the knowledge of company specific data then perhaps AAP has also provided some possible answers as to how human process planners perform their tasks. The same kind of reasoning for deciding the sequence of operations could also be employed for planning different products based on a different set of company data. AAP has illustrated the potentialities of applying AI techniques to process planning. The complexity of assembly can be tackled by breaking assemblies into sub-goals. The Modal Truth Criterion (MTC) was applied and tested in a real situation. A system for representing the logic of assembly was devised. A redundant goals elimination feature was also added in addition to the MTC in the AAP. Even though the ideal is a generative planner, in practice variant planners are still valid and perhaps closer to manual assembly process planning.

The overall aim of this study is to explore the nature of Industrial Additive Manufacturing Systems as implemented by commercial practitioners, with a specific focus on flexibility within the system and wider supply chain. This study is conducted from an Operations Management perspective to identify management implications arising from the application of contemporary Industrial Additive Manufacturing in the fulfilment of demand. The generation of the theoretical constructs and their evaluation is achieved through an abductive approach. The concept of an Industrial Additive Manufacturing System is developed, through which activities, enabling mechanisms, and control architectures are demonstrated. This is complimented by the proposal of a typology of flexibilities both for the manufacturing system and its supply chain. Twelve case studies are examined through practitioner interviews, observation, and mapping of the production processes at three Industrial Additive Manufacturing companies. These explorations are complimented by interviews with customers downstream of the Additive Manufacturer, and with interviews and a survey of principal upstream machine and material suppliers. This study identifies and classifies types of flexibility relevant to Industrial Additive Manufacturing Systems. It is shown that to achieve requisite flexibilities, it is necessary to manage the whole manufacturing system, not just individual machines. By extension, the internal manufacturing systems’ ability to achieve flexibility is shown to be both facilitated and constrained by the environment in which it operates. In particular, inadequacies in the supply of materials are shown to result in suboptimal practices within the manufacturing system. The principal contribution of this thesis is therefore the development of Industrial Additive Manufacturing from a manufacturing systems perspective, and an evaluation of its implications for flexibility.

A successful design is one of the most important elements for the commercial success of a product and the selection of appropriate materials is a key step within the product design process. The task is not easy; a large number of interacting factors, both technical and economic, need to be taken into consideration and a vast amount of data investigated. Product designers can benefit from using computer systems which can emulate the reasoning processes of an expert in selecting materials and provide ready access to appropriate materials data. The knowledge based system developed, Plassel fulfils the key requirements identified for such a system. It can: 1. Emulate the reasoning processes of a plastics expert. 2. Allow a customised data search to be undertaken 3. Access a range of data sources covering both embodiment and detail data. 4. Convert component functional requirements into property requirements. 5. Allow knowledge and experience to be stored in the system 6. Allow cost to be fully considered Professor Ashby in 1993 [1] stated "A full expert system for materials selection is decades away. Success has been achieved in specialised highly focused applications". Plasse1 is not such an application, it provides access to a full set of selection facilities. Novel aspects of Plassel include its ability to select on multi-dimensional criteria, automatically 'rate' materials and to conduct customised searches. Professor Ashby concludes with "It is only a question of time before more fully developed systems become available. They are something to keep informed about." Plassel is a more fully developed system for plastic materials selection than those currently available.

A major challenge for any manufacturer is including aspects of sustainable development in product design that are related to the social, environmental and economic impacts. Several methods and tools have been developed to facilitate sustainable product design, but they lack critical application of the ecological design (eco-design) process and economic costing, particularly during the conceptual design phase. This research overcomes these deficiencies by integrating eco-design approaches across all phases of product life cycle. These approaches were applied and tested in two case studies, which demonstrate that the tools developed can be used to reduce a product’s environmental and economic impacts while fulfilling customer needs. The integrated eco-design decision making (IEDM) methodology is proposed and developed in this study as a method for improving product sustainability. This is the principle contribution of this thesis to the field of sustainable product design. The IEDM applies environmental considerations across three stages of product development. The first stage is the life cycle assessment (LCA), which is used to identify critical areas in which the product’s environmental performance can be improved. The results of the LCA are then analysed in the second stage using an eco-design process (Eco-Process) model. This model identifies environmental concerns relating to the manufacturing process, product use, and end-of-life (EOL) strategy. These concerns are then addressed within the third stage, which uses an ecological house of quality (Eco-HoQ) embedded in an ecological quality function deployment (Eco-QFD) process. The ecodesign case-based reasoning (Eco-CBR) tool was also developed in this study to improve product design knowledge sharing. The development of the Eco-HOQ, which is integrated into the Eco-QFD process and part of the broader IEDM, is the second major contribution of this work. The Ecov HOQ is an extra “house” that can capture and manage sustainability considerations in a single place. This increases the relevance of the information used and produced in product design and encourages actions for improving sustainability at each phase of the Eco-QFD process. The Eco-QFD ensures that customer needs are incorporated within the context of sustainability. The eco-design case-based reasoning (Eco-CBR) tool was developed on the premise that if experiences from the Eco-QFD process can be captured in some useful form, designers can refer to and learn from past experiences. The Eco-CBR is an intuitive decision support tool that complements the IEDM framework and proposes solutions related to the social, environmental, and economic impacts of the product. The application of the entire IEDM framework, including the Eco-HoQ, Eco-QFD, and complementary Eco-CBR, is demonstrated in the case studies of single-use medical forceps and an office chair base. The case studies demonstrate the effectiveness of these tools when assessing a product’s sustainability, even when its design is altered. In addition, this methodology provides a complete view of the environmental performance and economic cost of these products over their entire life cycles in conjunction with an assessment of customer requirements. In summary, this thesis contributes significantly to the field of sustainable product design by proposing the integration of eco-design approaches at every stage of product development, including the critical conceptual phase. The approaches developed in this study will enable designers to improve product design, increase productivity, and reduce material usage and costs while meeting customer specifications.

A methodology of material characterisation and finite element model discretisation is presented for spot welded boron steel sheets, with the aim of predicting failure during quasi-static loading. The predicted load-displacement curves from the Finite Element model are compared with experimentally measured curves for lap-shear and cross-tension weld destructive geometries, and serve as model validation. During spot welding, the weld and surrounding material are exposed to a wide range of temperatures, from the melting point at the weld centre to room temperature in the base material. As a consequence, the weld exhibits varying microstructures with corresponding varying material properties which have a profound influence on its load bearing capacity and failure strength as a whole. In addition, boron steel spot welds exhibit unique hardness profiles, with high hardness values in the nugget and outlying base material, and a sudden drop in the area between these regions. This sudden decrease in material properties leads to further difficulties in modelling the failure of boron steel welds. The weld process inherently produces localised residual strains which also need to be accounted for in the model simulation, together with significant plastic strain redistributions resulting from the mechanical loading of the spot weld to its ultimate failure. The initial residual strains were measured in weld samples using neutron diffraction and were subsequently input into the FEA models. This thesis aims to quantify the varying material constitutive behaviour throughout the weld, required for the failure prediction. In particular, the following constitutive properties were extracted: the stress-strain response of certain weld regions, failure loci consisting of fracture strain versus stress state for the corresponding regions, and the residual stress distribution through the weld. Due to the small size of the weld, cutting test specimens directly from the weld is unachievable. To overcome this problem, specific weld and heat affected zone micro-structures were recreated onto practical tensile specimens through use of a Gleeble thermo-mechanical physical simulator. These specimens were subjected to the same thermal histories as specific points in the actual weld. From these tensile specimens, stress-strain curves relating to specific weld regions could be obtained. In a similar fashion, three additional destructive specimen sets were created to obtain failure loci. These failure loci give fracture strain as a function of stress state: specifically shear, uniaxial and plane-strain states. Due to the practical limitations in the accuracy of the Gleeble technique, deviations from the target microstructures were expected in the Gleeble samples. To gauge the extent of these deviations, a method of extracting reference material properties directly from the weld was required. Instrumented indentation offers such a solution, where the load and displacement of the indenter are measured and run through an algorithm to calculate the yield strength of the indented locations. These yield strengths are then compared with the yield from the Gleeble stress-strain curves to gauge the accuracy with which the weld microstructures were recreated. This technique serves to quantify the deviation of the Gleeble microstructures from the target material microstructures. It is common practice to discretise the weld into a small number of bulk regions during the design process, with material constitutive behaviour assigned to these discretised parts. In the new methodology, the extracted material constitutive behaviour is modelled as a continuously varying function of the distance from the weld centre. By performing appropriate interpolation, the data may be finely or roughly discretised. The data at a certain distance from the weld centre may then be assigned to the corresponding element in the finite element model. This means one may discretise the model by choosing the level of data interpolation refinement. The following results were observed in the thesis: • Residual strain distributions of boron steel spot welds, which have not been measured before, were presented. Clear correlations between hardness and residual stress distributions were seen. • A new application of instrumented indentation was attempted by verifying the accuracy of heat treated samples with respect to their target microstructures by comparing yield strengths. • The boron steel HAZ was characterised in a finer level of detail than seen in other literature works. • Through physical simulation, stress - strain and failure loci corresponding to certain HAZ areas were successfully extracted and used to model weld failure. • A new method of finite element model discretisation was presented, where material properties may be input as a relatively smooth function through the length of the model.

Frequent product model changes have become a characteristic feature in new product development and modern manufacturing. This has triggered a number of requirements such as shortening new product development time and production ramp-up time with simultaneous reduction of avoidable engineering changes and overall vehicle development cost. One of the most significant challenges when reducing new model development lead time is the large number of engineering changes, that are triggered by failures during production ramp-up stage but are unseen during design. In order to reduce engineering changes during ramp-up stage and also increase Right-First-Time development rate, there is a critical demand for improving quality of integrated product and production system design solutions. Currently, this is obtained by carrying out design synthesis which focuses on design optimization driven by computer simulation and/or physical experimentation. The design synthesis depends on the quality of the used surrogate models, which integrate critical product variables, (also known as Key Product Characteristics (KPCs)), with key process variables (Key Control Characteristics (KCCs)). However, a major limitation of currently existing surrogate models, used in design synthesis, is that these simply approximate underlying KPC-KCC relations with any deviation between the actual and predicted KPC assumed to be a simple random error with constant variance. Such an assumption raises major challenges in obtaining accurate design solutions for a number of manufacturing processes when: (1) KPCs are deterministic and non-linearity is due to interactions between process variables (KCCs) as is frequently the case in fixture design for assembly processes with compliant parts; (2) KPC stochasticity is either independent of (homo-skedastic) or dependent on (hetero-skedastic) on process variables (KCCs) and there is lack of physics-based models to confirm these behaviour; as can be commonly observed in case of laser joining processes used for automotive sheet metal parts; and, (3) there are large number of KCCs potentially affecting a KPC and dimensionality reduction is required to identify few critical KCCs as commonly required for diagnosis and design adjustment for unwanted dimensional variations of the KPC. This thesis proposes a generic Scalable Design Synthesis framework which involves the development of novel surrogate models which can address a varying scale of the KPC-KCC interrelations as indicated in the aforementioned three challenges. The proposed Scalable Design Synthesis framework is developed through three interlinked approaches addressing each aforementioned challenge, respectively: i. Scalable surrogate model development for deterministic non-linearity of KPCs characterized by varying number of local maximas and minimas. Application: Fixture layout optimization for assembly processes with compliant parts. This is accomplished in this thesis via (1) Greedy Polynomial Kriging (GPK), a novel approach for developing Kriging-based surrogate models for deterministic KPCs focusing on maximization of predictive accuracy on unseen test samples; and, (2) Optimal Multi Response Adaptive Sampling (OMRAS) a novel method of accelerating the convergence of multiple surrogate models to desired accuracy levels using the same training sample of KCCs. GPK surrogate models are then used for fixture layout optimization for assembly with multiple sheet metal parts. ii. Scalable surrogate model development for stochasticity characterized by unknown homo-skedastic or hetero-skedastic behaviour of KPCs. Application: In-process laser joining processes monitoring and in-process joint quality evaluation. Scalable surrogate model-driven joining process parameters selection, addressing stochasticity in KPC-KCC relations, is developed. A generic surrogate modelling methodology is proposed to identify and characterize underlying homo- and hetero-skedastic behaviour in KPCs from experimental data. This is achieved by (1) identifying a Polynomial Feature Selection (PFS) driven best-fitting linear model of the KPC; (2) detection of hetero-skedasticity in the linear model; and, (3) enhancement of the linear model upon identification of hetero-skedasticity. The proposed surrogate models estimate the joining KPCs such as weld penetration, weld seam width etc. in Remote Laser Welding (RLW) and their variance as a function of KCCs such as gap between welded parts, welding speed etc. in RLW. This information is then used to identify process window in KCC design space and compute joining process acceptance rate. iii. Scalable surrogate model development for high dimensionality of KCCs. Application: Corrective action of product failures triggered by dimensional variations in KPCs. Scalable surrogate model-driven corrective action is proposed to address efficient diagnosis and design adjustment of unwanted dimensional variations in KPCs. This is realized via (1) PFS to address high dimensionality of KCCs and identify a few critical ones closely related to the KPC of interest; and (2) surrogate modelling of the KPC in terms of the few critical KCCs identified by PFS; and, (3) two-step design adjustment of KCCs which applies the surrogate models to determine optimal nominal adjustment and tolerance reallocation of the critical KCCs to minimize production of faulty dimensions. All the aforementioned methodologies are demonstrated through the use of industrial case studies. Comparison of the proposed methods with design synthesis existing for the applications discussed in this thesis, indicate that scalable surrogate models can be utilized as key enablers to conduct accurate design optimization with minimal understanding of the underlying complex KPC-KCC relations by the user. The proposed surrogate model-based Scalable Design Synthesis framework is expected to leverage and complement existing computer simulation/physical experimentation methods to develop fast and accurate solutions for integrated product and production system design.

The Grain Silos and Flour Mills Organisation (GSFMO) is the responsible authority monopolising the Kingdom's milling industry. However, the organisation has recently been facing financial problems. The aim of this study is to estimate the technical, cost and allocative efficiency (TE, CE and AE) of the flour mills of the GSFMO (1988-2011), using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) approaches. In addition, it seeks to explain variation in efficiency levels between the mills and conduct further analysis through the second stage regression to estimate the effect of managerial variables. Productivity growth over time was also estimated in this study using DEA (2008-2011) and SFA (1988-2011) approaches. Both primary data and secondary data (1988-2011) to cover the nine milling branches were utilised. Using DEA under constant return to scale (CRS), average TE ranged from 91.72% in Khamis branch to 97.63% in Almadinah. Average TE under input-orientated variable return to scale (VRS) was lower than TE estimated under output-orientated VRS. The older branches had the lowest TE compared to newer branches. Under VRS, TE was greater than TE for the same branches under CRS. TE results using SFA were quite analogous to the results using DEA. Regarding productivity growth, using DEA for the 2008-2011 data, no consistent patterns were found across the GSFMO branches in the mean total factor productivity growth (TFPG), technical change (TC), and efficiency change (EC). When using SFA to estimate productivity growth over the period 1988 to 2011, there was a decrease in productivity growth for most branches. With regards to the results of the second stage regression, branch managers’ age, local temperature and 'bad' infrastructure have a significant negative relationship with TE, while manager's experience did not seem to have any significant relationship with TE. However, new and mix machine conditions and number of mills in each branch have a significant positive relationship with TE. In terms of CE and AE using the DEA approach, the results show that major losses incurred by the organisation were partly due to the significant decrease in CE and AE and that there is a significant scope to reduce inputs costs in the production process.