Analysis of manufacturing operations using knowledge- Enriched aggregate process planning

Knowledge-Enriched Aggregate Process Planning is concerned with the problem of supporting agile design and manufacture by making process planning feedback integral to the design function. A novel Digital Enterprise Technology framework (Maropoulos 2003) provides the technical context and is the basis for the integration of the methods with existing technologies for enterprise-wide product development. The work is based upon the assertion that, to assure success when developing new products, the technical and qualitative evaluation of process plans must be carried out as early as possible. An intelligent exploration methodology is presented for the technical evaluation of the many alternative manufacturing options which are feasible during the conceptual and embodiment design phases. 'Data resistant' aggregate product, process and resource models are the foundation of these planning methods. From the low-level attributes of these models, aggregate methods to generate suitable alternative process plans and estimate Quality, Cost and Delivery (QCD) have been created. The reliance on QCD metrics in process planning neglects the importance of tacit knowledge that people use to make everyday decisions and express their professional judgement in design. Hence, the research also advances the core aggregate planning theories by developing knowledge-enrichment methods for measuring and analysing qualitative factors as an additional indicator of manufacturing performance, which can be used to compute the potential of a process plan. The application of these methods allows the designer to make a comparative estimation of manufacturability for design alternatives. Ultimately, this research should translate into significant reductions in both design costs and product development time and create synergy between the product design and the manufacturing system that will be used to make it. The efficacy of the methodology was proved through the development of an experimental computer system (called CAPABLE Space) which used real industrial data, from a leading UK satellite manufacturer to validate the industrial benefits and promote the commercial exploitation of the research

Towards a machine enabled semantic framework for the distributed engineering design

The overall aim of this thesis is to identify and propose a suitable architectural framework for supporting cooperation processes and therefore enabling semantics within the distributed engineering design environment. The proposed architecture is intended to\ud characterize a software-based management of design related data, information and knowledge flows in the distributed engineering design organization. The aim is to provide a computational context for implementing ICT tools that would: (i) Minimise the effect of user and resource dispersion (particularly temporal and geographical dispersion), the misunderstandings that might be generated by the\ud (otherwise beneficial) functional and semantic distribution, the time spent for searching and retrieval of information, the effort of information translation between different tools and the administrational and organisational efforts not directly related to the design process (e.g. revision control) (ii) Maximise the quality of information (i.e. relevant information at relevant and appropriate times), knowledge sharing and reuse among distributed design\ud actors, the flexibility of the user interfaces and the designer’s time spent in the actual designing process.\ud In order to achieve the overall aim, the research work supporting this thesis was carried out along the following objectives:\ud 1. To investigate and characterize the engineering design process performed in a distributed environment and its problematic aspects;\ud 2. To research and study alternative theories for thinking and modelling the distributed engineering design process;\ud 3. To investigate current research in information and knowledge management for identifying supporting technologies for a possible solution to the identified\ud problematic aspects (from point 1);\ud 4. To analyze the requirement needs for a solution according to the findings from previous objectives, i.e. the driving problems (from point 1), the research and\ud therefore the thinking approach (from point 2), and available supporting technologies (from point 3);\ud 5. To synthesize the architectural framework along the identified supporting technologies (from point 3);\ud 6. To instantiate a software system along the underlying computational context as described by the architectural framework (from point 5)

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Towards automated restructuring of object oriented systems

The work introduces a method for diagnosing design flaws in object oriented systems, and finding meaningful refactorings to remove such flaws. The method is based on pairing up a structural pattern that is considered pathological (e.g. a code smell or anti-pattern) with a so called design context. The design context describes the design semantics associated to the pathological structure, and the desired strategic closure for that fragment. The process is tool supported and largely automated