Thesis (Doctoral)--Universität Osnabrücks, 2006. / Includes abstract and bibliographical references (p. 216-231).

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

The procedure for developing a new product, in general, is as follows. First, the design engineer must have a thorough understanding regarding the encountered problem. And, he must produce some design concepts based on the perceived requirements. Finally, some solutions are then achieved according to the prescribed design concepts. Unfortunately, few researchers have been able to explain, in a specific rather than abstract manner, the process of generating pertinent design concepts. However, this process has to be a very critical link in the chain. Without obtaining a good design concept the entire design procedure will fall, not to mention to find a suitable solution. In this research we use an interesting analogy between the design procedure and the well-familiarized Sun/Water-cycle system, to concretely describe the task of inspiration of innovative concepts particularly in engineering design. The use of this analogy, we believe, will guide engineers to more effectively and more efficiently go through the stages of conceptual design. Consequently, the entire product development time can be reduced.

The basic concept of this work is that functional modelling techniques are applicable to and of practical use in, producing a qualitative model of conceptual engineering design. A qualitative function based model of conceptual design has been developed and a computer based implementation has been built and tested. The rationale behind the modelling scheme and the computer implementation are described in detail. In addition to a review of existing models of design the research provides a significant new capability in four main areas: • An ability to generate new concepts with a controlled degree of similarity to existing designs. • A new function based model of engineering conceptual design. • The COncept Design ASsistant (CODAS) system, a computer based implementation of the function based model, has been developed and tested. • A new symbolic representation language. CODAS is a hybrid case-based and function-based modelling system, implemented in the domain of mechanical device design, which demonstrates the practical application of this new model. The CODAS system aims to provide a design support tool which can invent both routine and novel devices based on experience gained from past successful design solutions. Fast and efficient data handling is achieved by utilizing Case Based Reasoning (CBR) technology to store and retrieve past design solutions which are defined in terms of a symbolic representation language. The underlying design model is function based and employs a technique of divergent function to form mapping to produce physical embodiments of the proposed functional solutions.

When faced with a new design problem, Engineers most often tend to rely on their accumulated knowledge of science, mathematics, and appropriate experience to reach suitable solutions. Case-Based Reasoning is a new engineering paradigm that reflects this fact by suggesting solutions to novel problems based on the recall and reuse of specific experiences. Such a paradigm relies on previous successful design solutions that are stored in the form of separate cases in a database. / The aim of this thesis is to develop a process that will provide examples which can be used to set up a database of optimized designs for various electromagnetic devices such as loudspeakers and actuators. Each stored design will represent an optimum solution to a specific set of requirements for an electromagnetic device. These designs will eventually be used by a case-based reasoning system to reach a solution for possible requested future designs. The process will also involve developing a parameterization of a particular class of devices as well as testing optimization processes to be applied to the initial designs in order to ensure that the solutions stored in the case database represent effective and realistic devices which satisfy the requirements. This thesis also presents test results that illustrate how each optimized design conforms to certain requirements set as an input objective.

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No description available.

A central problem in case based reasoning (CBR) is how to store and retrieve cases. One approach to this problem is to use exemplar based models, where only the prototypical cases are stored. However, the development of an exemplar based model (EBM) requires the solution of several problems: (i) how can a EBM be represented? (ii) given a new case, how can a suitable exemplar be retrieved? (iii) what makes a good exemplar? (iv) how can an EBM be learned incrementally? This thesis develops a new model, called a probabilistic exemplar based model, that addresses these research questions. The model utilizes Bayesian networks to develop a suitable representation and uses probability theory to develop the foundations of the developed model. A probability propagation method is used to retrieve exemplars when a new case is presented and for assessing the prototypicality of an exemplar. The model learns incrementally by revising the exemplars retained and by updating the conditional probabilities required by the Bayesian network. The problem of ignorance, encountered when only a few cases have been observed, is tackled by introducing the concept of a virtual exemplar to represent all the unseen cases. The model is implemented in C and evaluated on three datasets. It is also contrasted with related work in CBR and machine learning (ML).

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Case-Based Reasoning (CBR) has been successfully applied to many fields especially in the design domain. Poor assembly part design increases the cost, raises the manufacturing complexity and reduces the product quality. However, little research has been devoted to predict the potential design problems in the early design stage. The objective of this paper is to integrate the indexes of assemblability and manufacturability into adaptive phase in CBR to avoid inexperienced mistakes. Early experimental results indicate that quantitative feedback of these indexes can guide novices to depict a good assembly part design, let experienced designers confirm their experience judgments and finally impart the experience to novices through CBR methodology.