Over 70% of final product costs are determined by initial product design. Hence, to depict a capable and reliable part design is important during the part conceptual design phase. Case-Based Reasoning (CBR), based on analogical reasoning, is a problem solving methodology. This paper proposes the CBR architecture applied to assembly part design for effectively managing previous design experience and evaluating assemblability and manufacturability of assembly part design. The results can provide a useful reference for novices to reuse and revise previous experience and experienced designers to impart their expertise through CBR methodology.

Concrete design rules can facilitate the designer to depict capable design and reliable products. However, it is difficult to deduce systematic design rules from previous experience and to modify the rules in a rule-based system. In the last decade, Case-Based Reasoning (CBR) has become an important methodology to solve the problem. The objective of this paper is to determine the appropriate case representation used in assembly part design for developing a CBR system. The designer can obtain real-time Early experimental results indicate that the case representation can appropriately represent the expertise and experience of assembly part design based on CBR methodology.

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Classification is a general framework that can be applied tovarious tasks such as object recognition, prediction, diagnosis or learning. There exist at least two different approaches for classification, namely rule-based and similarity-based classification. The two approaches have different strengths and weaknesses. The former requires a domain theory in order to make inferences from the test instance to its class. The latter does not have this requirement and approximates the class of a test instance via its similarity to a set of known instances.In this thesis the above two approaches are integrated in the realm of Case-Based Reasoning (CBR). CBR treats new cases according to their similarity to stored cases. Similarity is calculated by a similarity measure, which is the crucial factor for classification accuracy. In this work, rule-based domain knowledge is incorporated into the similarity measures of CBR in order to increase classification accuracy. Several novel integration methods are introduced, implemented and evaluated. Since knowledge about real world domains is typically imperfect, the approach does not assume that the domain theories are accurate or complete. Rather, a systematic analysis of different knowledge types is presented that shows the effect of imperfect knowledge on classification accuracy. The analysis is conducted partly empirically in artificial and in real world domains, and partly formally.

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This study investigated the various methods involved in creating an intelligent tutor for the University of Central Florida Infinity Web Applets (UCF Infinity Web Applets). After conducting research into various methods, two major methods emerged and they are: solving the problem for the student and helping the student when they become stymied and unable to solve the problem. A storyboard was created to show the interactions of the student and system along with a list of features that were desired to be included in the tutoring system. From the storyboard and list of features, an architecture was created to handle all of the interactions and features. After the initial architecture was designed, the development of the actual system was started. The architecture underwent a multitude of changes to conclude with a working system, EINO. The final architecture of EINO incorporated a case based reasoning system to perform pattern recognition on the student's input into the UCF Infinity Web Applets. The interface that the student interacts with was created using flash. EINO was implemented in three of the labs from the UCF Infinity Web Applets. A series of tests were performed on the EINO tutoring system to prove that the system could actually perform each and every one of the features listed initially. The final test was a simulation of how the EINO would perform under a set of given cases. Test subjects with the same educational level as the target group were chosen to spend an unlimited time using each of the three labs. Each of the test subjects filled out a survey on every lab to determine if the EINO system produced a helpful output.

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Fixtures accurately locate and secure a part during machining operations such that the part can be manufactured to design specifications. To reduce the design costs associated with fixturing, various computer-aided fixture design (CAFD) methods have been developed through the years to assist the fixture designer. Much research has been directed towards developing systems that determine an optimal fixture plan layout, but there is still a need to develop a CAFD method that can continue to assist designers at the unit level where the key task is identifying the appropriate structure that the individual units comprising a fixture should take. This research work details the development of a CAFD methodology (called CAFixD) that seeks to fill this hole in the CAFD field. The approach taken is to consider all operational requirements of a fixture problem, and use them to guide the design of a fixture at the unit level. Based upon a case-based reasoning (CBR) methodology where relevant design experience is retrieved and adapted to provide a new fixture design solution, the CAFixD methodology adopts a rigorous approach to indexing design cases in which axiomatic design functional requirement decomposition is adopted. Thus, the design requirement is decomposed in terms of functional requirements, physical solutions are retrieved and adapted for each individual requirement, and the design re-constituted to form a complete fixture design. Case adaptation knowledge is used to guide the retrieval process. Possible adaptation strategies for modifying candidate cases are identified and then evaluated. Case and adaptation strategy combinations that result in adapted designs that best satisfy the preferences of the designer are used as the final design solutions. Possible means of refining the effectiveness of the method include combining adaptation strategies and considering the order in which design decisions are taken.