No / This paper presents a Knowledge-Based (KB) integrated approach for planning and designing of number of machining centres, selection of material handling system, layout and networking architecture and cost analysis for a Flexible Manufacturing Systems (FMS). The KB model can be applied for integrating the decision issues at both the planning and designing stages of an FMS for three types of layouts (single row, double row, and loop) and three MHS types (robot-conveyor, AGV-conveyor and a hybrid AGV-robot-conveyor). The KB methodology starts from a suitable information input, which includes demand per year of part types, part type’s information, machining centre’s calculation, Material Handling System (MHS) selection, machining centre’s layout selection, networking selection and financial analysis. The KB methodology is developed by using AM, an expert system shell, and contains over 1500 KB rules. The performance of the system has been verified and validated through four published and four industrial case studies, respectively. The validation results from industry show that the KB methodology is capable of considering detailed design inputs and is able to assist in designing and selecting a practical FMS. It is concluded that a KB system for the present FMS application is a viable and efficient methodology.

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Yes / The importance of maintenance has escalated significantly by the increase in automation in manufacturing processes. This condition changed the perspective of maintenance from being considered as an inevitable cost to being seen as a key business function to drive competitiveness. Consequently, maintenance decisions need to be aligned with the business competitive strategy as well as the requirements of manufacturing/quality functions in order to support manufacturing equipment performance. Therefore, it is required to synchronise the maintenance strategy and operations with business and manufacturing/quality aspects. This article presents the design and development of a Knowledge Based System for Integrated Maintenance Strategy and Operations. The developed framework of the Knowledge Based System for Integrated Maintenance Strategy and Operations is elaborated to show how the Knowledge Based System for Integrated Maintenance Strategy and Operations can be applied to support maintenance decisions. The knowledge-based system integrates the Gauging Absences of Prerequisites methodology in order to deal with different decision-making priorities and to facilitate benchmarking with a target performance state. This is a new contribution to this area. The Knowledge Based System for Integrated Maintenance Strategy and Operations is useful in reviewing the existing maintenance system and provides reasonable recommendations for maintenance decisions with respect to business and manufacturing perspectives. In addition, it indicates the roadmap from the current state to the benchmark goals for the maintenance system. / Ministry of Research, Technology and Higher Education of the Republic of Indonesia and the University of Bradford, UK.

This thesis considers various aspects of the use of remote sensing, geographical information systems and Bayesian knowledge-based expert system technologies for broad-scale monitoring of land condition in the Western Australian wheat belt.The use of remote sensing technologies for land condition monitoring in Western Australia had previously been established by other researchers, although significant limitations in the accuracy of the results remain. From a monitoring perspective, this thesis considers approaches for improving the accuracy of land condition monitoring by incorporating other data into the interpretation process.Digital elevation data provide one potentially useful source of information. The use of digital elevation data are extensively considered here. In particular, various methods for deriving variables relating to landform from digital elevation data and remotely sensed data are reviewed and new techniques derived.Given that data from a number of sources may need to be combined in order to produce accurate interpretations of land use/condition, methods for combining data are reviewed. Of the many different approaches available, a Bayesian approach is adopted.The approach adopted is based on relatively new developments in probabilistic expert systems. This thesis demonstrates how these new developments provide a unified framework for uniting traditional classification methods and methods for integrating information from other spatial data sets, including data derived from digital elevation models, remotely sensed imagery and human experts.Two applications of the techniques are primarily considered. Firstly, the techniques are applied to the task of salinity mapping/ monitoring and compared to existing techniques. Large improvements are apparent. Secondly, the techniques are applied to salinity prediction, an application not previously considered by ++ / other researchers in this domain. The results are encouraging. Finally limitations of the approach are discussed.

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Yes / Global manufacturing industry mostly depends on new product development and processes to become competitive. The product development process for automotive industry is normally complicated, lengthy, expensive, and risky. Hence, a study of lean manufacturing processes for low volume manufacturing in automotive industry is proposed to overcome this issue by eliminating all wastes in the lengthy process. This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for lean process in Low Volume Automotive Manufacturing (LVAM). The research concentrates on the low volume processes by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potential waste elements of low volume process manufacturing. The KB system analyses the difference between the existing and the benchmark standards for lean process for an effective implementation through the GAP analysis technique. The proposed model explores three major lean process components, namely Employee Involvement, Waste Elimination, and Kaizen (continuous improvement). These three components provide valuable information in order for decision makers to design and implement an optimised low volume manufacturing process, but which can be applied in all process manufacturing, including chemical processing.

Yes / The current healthcare systems have numerous gaps that need to be filled to reach the best practice. This paper presents a novel approach to design and validate a hybrid knowledge-based system (KBS) to evaluate QM of healthcare environment (QMHE) using a hybrid system that has not been used before. It will be combined with gauge absence perquisite (GAP) method to sustain a successful operation of the large number of key performance indicators (KPIs) that involved in QMHE and to detect the gap between each KPI and the anticipated point. Out of 354 KB rules answered, the system has categorised 225 as GPs and the remaining 128 as BPs. The 128 bad points are categorised into different problem categories (20 PC-1, 34 PC-2, 34 PC-3, 40 PC-4, and 0 PC-5) where they represent the actions that need to be enhanced to reach the desired level of quality management.

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