In many sectors of commercial operation, the scheduling of workflows and the allocation of resources at an optimum time is critical; for effective and efficient operation. The high degree of complexity of a “Job Shop” manufacturing environment, with sequencing of many parallel orders, and allocation of resources within multi-objective operational criteria, has been subject to several research studies. In this thesis, a scheduling system for optimizing multi-objective job shop scheduling problems was developed in order to satisfy different production system requirements. The developed system incorporated three different factors; setup times, alternative machines and release dates, into one model. These three factors were considered after a survey study of multiobjective job shop scheduling problems. In order to solve the multi-objective job shop scheduling problems, a combination of genetic algorithm and a modified version of a very recent and computationally efficient approach to non-dominated sorting solutions, called “efficient non-dominated sort using the backward pass sequential strategy”, was applied. In the proposed genetic algorithm, an operation based representation was designed in the matrix form, which can preserve features of the parent after the crossover operator without repairing the solution. The proposed efficient non-dominated sort using the backward pass sequential strategy was employed to determine the front, to which each solution belongs. The proposed system was tested and validated with 20 benchmark problems after they have been modified. The experimental results show that the proposed system was effective and efficient to solve multi-objective job shop scheduling problems in terms of solution quality.

Modern competitive market demands frequent change in product variety, increased production volume and shorten product/process change over time. These market requirements point towards development of key enabling technologies (KETs) to shorten product and process development cycle, improved production quality and reduced time-to-launch. One of the critical prerequisite to develop the aforementioned KETs is efficient and accurate modelling of product and process dimensional errors. It is especially critical for assembly processes with compliant parts as used in automotive body, appliance or wing and fuselage assemblies. Currently, the assembly process is designed under the assumption of ideal (nominal) products and then check by using variation simulation analysis (VSA). However, the VSA simulations are oversimplified as they are unable to accurately model or predict the effects of geometric and dimensional variations of compliant parts, as well as variations of key characteristics related to fixturing and joining process. This results in product failures and/or reduced quality due to un-modelled interactions in assembly process. Therefore, modelling and prediction of the geometric shape errors of complex sheet metal parts are of tremendous importance for many industrial applications. Further, as production yield and product quality are determined for production volume of real parts, thus not only shape errors but also shape variation model is required for robust assembly system development. Currently, parts shape variation can be measured during production by using recently introduced non-contact gauges which are fast, in-line and can capture entire part surface information. However, current applications of non-contact scanners are limited to single part inspection or reverse engineering applications and cannot be used for monitoring and statistical process control of shape variation. Further, the product shape variation can be reduced through appropriate assembly fixture design. Current approaches for assembly fixture design seldom consider shape variation of production parts during assembly process which result in poor quality and yield. To address the aforementioned challenges, this thesis proposes the following two enablers focused on modelling of shape errors and shape variation of compliant parts applicable during assembly process design phase as well as production phase: (i) modelling and characterisation of shape errors of individual compliant part with capabilities to quantify fabrication errors at part level; and (ii) modelling and characterisation of shape variation of a batch of compliant parts with capabilities to quantify the shape variation at production level. The first enabler focuses on shape errors modelling and characterisation which includes developing a functional data analysis model for identification and characterisation of real part shape errors that can link design (CAD model) with manufacturing (shape errors). A new functional data analysis model, named Geometric Modal Analysis (GMA), is proposed to extract dominant shape error xixmodes from the fabricated part measurement data. This model is used to decompose shape errors of 3D sheet metal part into orthogonal shape error modes which can be used for product and process interactions. Further, the enabler can be used for statistical process control to monitor shape quality; fabrication process mapping and diagnosis; geometric dimensioning and tolerancing simulation with free form shape errors; or compact storage of shape information. The second enabler aims to model and characterise shape variation of a batch of compliant parts by extending the GMA approach. The developed functional model called Statistical Geometric Modal Analysis (SGMA) represents the statistical shape variation through modal characteristics and quantifies shape variation of a batch of sheet metal parts a single or a few composite parts. The composite part(s) represent major error modes induced by the production process. The SGMA model, further, can be utilised for assembly fixture optimisation, tolerance analysis and synthesis. Further, these two enablers can be applied for monitoring and reduction of shape variation from assembly process by developing: (a) efficient statistical process control technique (based on enabler ‘i’) to monitor part shape variation utilising the surface information captured using non-contact scanners; and (b) efficient assembly fixture layout optimisation technique (based on enabler ‘ii’) to obtain improved quality products considering shape variation of production parts. Therefore, this thesis proposes the following two applications: The first application focuses on statistical process control of part shape variation using surface data captured by in-process or off-line scanners as Cloud-of-Points (CoPs). The methodology involves obtaining reduced set of statistically uncorrelated and independent variables from CoPs (utilising GMA method) which are then used to develop integrated single bivariate T2-Q monitoring chart. The joint probability density estimation using non-parametric Kernel Density Estimator (KDE) has enhanced sensitivity to detect part shape variation. The control chart helps speedy detection of part shape errors including global or local shape defects. The second application determines optimal fixture layout considering production batch of compliant sheet metal parts. Fixtures control the position and orientation of parts in an assembly process and thus significantly contribute to process capability that determines production yield and product quality. A new approach is proposed to improve the probability of joining feasibility index by determining an N-2-1 fixture layout optimised for a production batch. The SGMA method has been utilised for fixture layout optimisation considering a batch of compliant sheet metal parts. All the above developed methodologies have been validated and verified with industrial case studies of automotive sheet metal door assembly process. Further, they are compared with state-of-the-art methodologies to highlight the boarder impact of the research work to meet the increasing market requirements such as improved in-line quality and increased productivity.

There are two main threads of this thesis, namely, an unknown (unmeasurable) input reconstruction and fault detection and diagnosis. The developed methods are in the form of parity equations, i.e. finite impulse response filters of the available input and output measurements. In the first thread the design of parity equations for the purpose of an unknown input reconstruction of linear, time-invariant, discrete-time, stochastic systems is taken into consideration. An underlying assumption is that both measurable system inputs as well as the outputs can be subjected to noise, which leads to an errors-in-variables framework. The main contribution of the scheme is accommodation of the Lagrange multiplier method in order to minimise the influence of the noise on the unknown input estimate. Two potential applications of the novel input reconstruction method are proposed, which are a control enhancement of a hot strip steel rolling mill and an estimation of a pollutant level in a river. Furthermore, initial research is conducted in the field of the unknown input recon- struction for a class of nonlinear systems, namely, Hammerstein-Wiener systems, where a linear dynamic block is preceded and followed by a static nonlinear function. Many man-made as well as naturally occurring systems can be accurately described using Hammerstein-Wiener models. However, it is considered that not much attention has been paid to Hammerstein-Wiener systems in the errors-in-variables framework and in this thesis it is aimed to narrow this gap. The second thread considers a problem of robust (disturbance decoupled) fault de- tection as well as fault isolation and identification. Unmeasurable external stimuli, parameter variations or discrepancies between the system and the model act as distur- bances, which can obstruct the fault detection process and lead to false alarms. Thus, a fault detection filter needs to be decoupled from the disturbances. In this thesis the right eigenstructure assignment method used for the robust fault detection filter design is extended to systems with unstable invariant zeros. Another contribution re- gards the design of robust parity equations of any arbitrary order using both left and right eigenstructure assignment. Furthermore, a parity equation-based fault isolation and identification filter is designed which provides an estimate of the fault. A simple method for the calculation of thresholds whose violation indicates a fault occurrence is also proposed for the errors-in-variables framework.

In manufacturing, automation has replaced many dangerous, mundane, arduous and routine manual operations, for example, transportation of heavy parts, stamping of large parts, repetitive welding and bolt fastening. However, skilled operators still carry out critical manual processes in various industries such as aerospace, automotive and heavy-machinery. As automation technology progresses through more flexible and intelligent systems, the potential for these processes to be automated increases. However, the decision to undertake automation is a complex one, involving consideration of many factors such as return of investment, health and safety, life cycle impact, competitive advantage, and resources and technology availability. A key challenge to manufacturing automation is the ability to adapt to process variability. In manufacturing processes, human operators apply their skills to adapt to variability, in order to meet the product and process specifications or requirements. This thesis is focussed on understanding the 'variability involved in these manual processes, and how it may influence the automation solution'. Two manual industrial processes in polishing and de-burring of high-value components were observed to evaluate the extent of the variability and how the operators applied their skills to overcome it. Based on the findings from the literature and process studies, a framework was developed to categorise variability in manual manufacturing processes and to suggest a level of automation for the tasks in the processes, based on scores and weights given to the parameters by the user. The novelty of this research lies in the creation of a framework to categorise and evaluate process variability, suggesting an appropriate level of automation. The framework uses five attributes of processes; inputs, outputs, strategy, time and requirements and twelve parameters (quantity, range or interval of variability, interdependency, diversification, number of alternatives, number of actions, patterned actions, concurrency, time restriction, sensorial domain, cognitive requisite and physical requisites) to evaluate variability inherent in the process. The level of automation suggested is obtained through a system of scores and weights for each parameter. The weights were calculated using Analytical Hierarchical Process (AHP) with the help of three experts in manufacturing processes. Finally, this framework was validated through its application to two processes consisting of a lab-based peg-in-a-hole manual process and an industrial process on welding. In addition, the framework was further applied to three processes (two industrial processes and one process simulated in the laboratory) by two subjects for each process to verify the consistency of the results obtained. The results suggest that the framework is robust when applied by different subjects, presenting high similarity in outputs. Moreover, the framework was found to be effective when characterising variability present in the processes where it was applied. The framework was developed and tested in manufacturing of high value components, with high potential to be applied to processes in other industries, for instance, automotive, heavy machinery, pharmaceutical or electronic components, although this would need further investigation. Thus, future work would include the application of the framework in processes in other industries, hence enhancing its robustness and widening its scope of applicability. Additionally, a database would be created to assess the correlation between process variability and the level of automation.

The effect of national culture on the performance of teams is becoming an increasingly important issue in advanced western countries. There are many interlinked reasons for this, including the increasing globalisation of companies and the use of joint ventures for the development of expensive platforms. A further issue relates to the export of complex sociotechnical systems, where a culture clash between designer/manufacturer and user can lead to significant problems. This report describes research work that was carried out to analyse the cultural factors that influence the performance of teams (including researchers, designers, operators and crews), and to determine whether these factors could be captured in a tool to provide assistance to team managers and team builders. The original point of interest related to the development of increasingly complex sociotechnical systems, for example nuclear power stations, oil refineries, offshore oil platforms, hospital systems and large transport aircraft. Answers that might be sought, in particular by the senior managers of global companies, include (1) the best teams (or best national locations) for fundamental research, industrial research & development, product/system improvement and other key activities, and (2) the implications for system performance and, as a result, for system design, of targeting an eastern Asian market, a South-American market, etc. A literature review was carried out of the effects of culture on team performance, of culture measures and tools and of task classifications; in addition, empirical evidence of the validity of measures and tools was sought. Significant evidence was found of the effects of culture on teams and crews, but no national culture-based team performance prediction tools were found. Based on the results of the literature review, Hofstede's original four-dimension cultural framework was selected as the basis for the collection and analysis of empirical data, including the results of studies from the literature and the researcher s own empirical studies. No team or task classification system was found that was suitable for the purposes of linking culture to team performance, so a five-factor task classification was developed, based on the literature review, to form the basis of the initial modelling work. A detailed analysis of results from the literature and from the author s pilot studies revealed additional culture-performance relationships, including those relating to cultural diversity. Three culture-performance models were incorporated into software tools that offered performance prediction capabilities. The first model was primarily a test bed for ideas; the second model incorporated a task/behavioural approach which achieved limited success; the third and final model was evaluated against a range of team and crew performance data before being tested successfully for acceptability by users. The research results included the discovery that the effects of cultural diversity must be sought at the individual cultural dimension level not at the composite level, that the effects of national culture on team performance are consistent and strong enough to be usefully captured in a predictive culture tool and that the relationship between culture and behaviour is moderated by contextual factors.

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The continuing trends of miniaturisation, mass customisation, globalisation and wide use of the Internet have great impacts upon manufacturing in the 21st century. Micro manufacturing will play an increasingly important role in bridging the gap between the traditional precision manufacturing and the emerging technologies like MEMS/NEMS. The key requirements for micro manufacturing in this context are hybrid manufacturing capability, modularity, reconfigurability, adaptability and energy/resource efficiency. The existing design approaches tend to have narrow scope and are largely limited to individual manufacturing processes and applications. The above requirements demand a fundamentally new approach to the future applications of micro manufacturing so as to obtain producibility, predictability and productivity covering the full process chains and value chains. A novel generic modular reconfigurable platform (GMRP) is proposed in such a context. The proposed GMRP is able to offer hybrid manufacturing capabilities, modularity, reconfigurablity and adaptivity as both an individual machine tool and a micro manufacturing system, and provides a cost effective solution to high value micro manufacturing in an agile, responsive and mass customisation manner. An integrated framework has been developed to assist the design of GMRPs due to their complexity. The framework incorporates theoretical GMRP model, design support system and extension interfaces. The GMRP model covers various relevant micro manufacturing processes and machine tool elements. The design support system includes a user-friendly interface, a design engine for design process and design evaluation, together with scalable design knowledge base and database. The functionalities of the framework can also be extended through the design support system interface, the GMRP interface and the application interface, i.e. linking to external hardware and/or software modules. The design support system provides a number of tools for the analysis and evaluation of the design solutions. The kinematic simulation of machine tools can be performed using the Virtual Reality toolbox in Matlab. A module has also been developed for the multiscale modelling, simulation and results analysis in Matlab. A number of different cutting parameters can be studied and the machining performance can be subsequently evaluated using this module. The mathematical models for a non-traditional micro manufacturing process, micro EDM, have been developed with the simulation performed using FEA. Various design theories and methodologies have been studied, and the axiomatic design theory has been selected because of its great power and simplicity. It has been applied in the conceptual design of GMRP and its design support system. The implementation of the design support system is carried out using Matlab, Java and XML technologies. The proposed GMRP and framework have been evaluated through case studies and experimental results.

The aim of the research is to develop a methodological analysis of problems in the area of design for manufacture in low volume high complex products found in the writer’s workplace. The majority of research in this area has been around high volume products, such as automotive products and little consideration has been given to designing complex products from industries like aircraft manufacture. This research evaluates design for manufacture (DFM) information in the design lifecycle (DLC). The author’s research introduces a unique DLC process, one which structures decisions and data transfer through the DLC. The research also looks at current academic work and introduces industrial issues present in today’s environment. It is crucial to the design of a product to select the appropriate design environment in which it operates, as it will structure the way the engineering activities are established and developed. It is also important for the organisation to decide on the environment in which the design definition should evolve. Therefore the research reviews the different design definition environments, these were carefully analysed by the author. The evaluation of a design to ensure its manufacturability is a major element in the research, a review of previous work has highlighted that within current publications there has been little work in this area. The research has developed a methodology to evaluate the robustness of a design. It not only looks at the engineering design but also evaluates its adherence to customer requirements and the effect on cost for the overall product life-cycle. It also considers industrial needs for a reduction in the length of design life-cycle, while ensuring a reduction in manufacturing costs. There are two main contributors to this, firstly the use of key characteristics and secondly, the ability to control the manufacturability of a design. The author has developed a novel software tool enabling efficient evaluation of a design. The author discusses his contribution to existing knowledge in three main areas of the research. The most significant being the introduction of a tool to evaluate a design early in the design life-cycle to ensure manufacturability. To validate the research the author introduces the reader to three experimental phases. He validates his methodology by analysing the design of various aircraft assemblies discussing his findings of how manufacturable the designs are. This leads to the conclusion that the author’s research adds substantial knowledge to the area of design for manufacture.

La presente investigación se realizó en las instalaciones de la empresa Fabricaciones Metálicas FAMETAL S.A.C., una empresa peruana del sector manufacturero dedicada a la producción de estructuras metálicas y mobiliario de melamina. Esta empresa presentaba métodos de trabajos no estandarizados que duraban 590,41 minutos por unidad y tiempos no productivos de 25,74 minutos por unidad que limitaban la producción a 1790 unidades por año, asimismo un balance de línea empírico con el que trabajaban presentaba una eficiencia del 74%, la ganancia por la venta de cada producto era de 34 céntimos de sol por cada sol invertido. Ante esta problemática se determinó estandarizar el método de producción a través de la asignación de procedimientos a los operarios y el uso de tiempos estándar, lo cual redujo el tiempo de proceso en 55,64%; se realizó una nueva distribución de planta mediante los métodos SLP y de Güerchet, que redujo los tiempos de transportes en 49,31%, y se balanceó la línea de producción de muebles de melamina logrando reducir el tiempo de ciclo en 55,25%, se aumentó la producción en 123,46% y elevando la eficiencia de línea a 89,74%. Finalmente se analizó la rentabilidad de la propuesta de mejora, la cual presenta una relación beneficio/costo de 1,21, y se aumentó la eficiencia económica de la producción de muebles de melamina de 1,34 a 1,54.

En el distrito de Pomalca se encuentra ubicado el Matadero Municipal de categoría 1, en el cual se realizó esta investigación. El diagnóstico del establecimiento se realizó mediante hojas de verificación, permitió concluir que el establecimiento no cumple con el 66,67 % de los requisitos de ubicación y con el 84,38% de las áreas establecidas según el DS 015-2012 AG. Asimismo se detectó que la planta actual cuenta con demanda insatisfecha de sacrificio de ganado vacuno. Para definir la capacidad del nuevo establecimiento se determinó la demanda proyectada, la cual fue de 4 410 cabezas de ganado vacuno, 1 493 cabezas de ganado porcino y 465 caprinos. El nuevo cupo de faena del ganado vacuno será de 15 cabezas convirtiéndolo en un establecimiento de segunda categoría, según la clasificación de SENASA. Ante esto, el objetivo de la investigación fue diseñar un nuevo camal municipal de categoría II que cumpla con los requisitos del DS 015-2012 AG para satisfacer la demanda. En cuanto al diseño del camal, se determinó que la ubicación del nuevo matadero será fuera de la zona de crecimiento urbano determinada mediante el mapa de zonificación ecológica del distrito. El nuevo establecimiento requiere 1 950 m2, determinado mediante dos criterios: el método de Guerchet y el reglamento vigente. El costo de inversión para la implementación del matadero es de S/ 710 054,4 bajo la modalidad de un PIP, la nueva planta de faena será rentable con un VANS de S/ 124 644,43 y un TIRS de 10 %.