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No description available.

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Manual tuning of electronic filters represents a time-consuming process which can benefit from some computer assistance. A prototype computer-based system for the tuning of crystal filters after manufacture was developed. This system solved the problem of crystal filter tuning in a novel way. The system, called AEK (Applied Expert Knowledge), was developed using crystal filters and is a hybrid system with the following two functions: (1) Required values of features are extracted from the filter waveform and passed to the expert system which determines the component to adjust and the direction to turn, or the end of the tuning. (2) Sampled values of the waveform are extracted and passed to a neural network which determines how far to turn the component chosen in (1). The prominent aspects were: - Work using the protocol analysis elicitation technique indicated the need to separate the process into two sub-tasks (stopband and passband). Each sub-task was divided into three classification parts which determined (i) the continuation of the tuning process, (ii) the component and direction to turn, and (iii) the distance to turn respectively. Unfortunately, it was not possible to extract rules from the operator. - Three learning techniques (IID3, Adaptive Combiners, Neural Networks) were used and compared as the means of automated knowledge elicitation. All three techniques used case knowledge in the form of examples. The investigations suggested the use of ID3 for the first two parts of each subtask employing features with linguistic values. The number of linguistic values each feature has, was also derived. - Neural networks were trained for the third part. It was necessary to have one network for each component/direction combination and to use examples from just one mal-adjusting process. - Tests of the hybrid system for a number of cases indicated that it performed as well as a skilled operator, and that it can be used by novice operators but situations arose where there was either no knowledge or contradictory knowledge. The prototype system was developed using one type of crystal filters but the generic construction procedure can be followed to build other systems for other types.

In recent years considerable attention has been diverted towards devising new strategies to deal with the competitive nature of manufacturing environments. Such strategies are often influenced by the costs and quality of the manufactured products. An effective tool management and control system can significantly contribute to the efficiency of manufacturing facilities by maintaining the flow of production, reducing manufacturing costs, and be instrumental to the quality of finished goods. Most companies however, have consistently overlooked the importance of tooling and its impact on the efficiency of their manufacturing facilities, consequently it has become a maior production bottleneck. Hence, the need for uncovering the nature, extent, and underlying causes of tooling problems. Having recognised the importance of a Computer Aided Tool Management And Control Systems (CATMACS) as a partial solution to the efficient management of tooling resources, the study then looks at the implementation of CATMACS in fourteen manufacturing companies in the UK, developing some 40 propositions. Based on the developed propositions, a framework for the implementation methodology is constructed. The framework consists of five phases; Tool audit, Strategy, Design, Action, and Review. The framework has been evaluated and the inputs and outputs to the phases have been identified. The framework represents a significant step in understanding of CATMACS implementation, in particular: <ul> <li>It addresses the need for such system.</li> <li>It provides the basis of an implementation toolkit.</li> <li>It provides guidance for the best way of implementing a CATMACS.</li> <li>It is constructed using hard data.</li> </ul>