Knowledge Representation and Reasoning Methodology based on CBR Algorithm for Modular Fixture Design

Abstract

CBR algorithm provides a better knowledge transfer and explanation than rule-based inference. It solves new problems by adapting solutions that were used to solve old problems. Based on CBR algorithm, a methodology applied in modular fixture design and focus on workpiece locating is proposed in this study. A similar solution can be retrieved from past experiences. Evaluation is applied for this retrieved case by checking degrees of freedom (DOF) to determine whether it is satisfactory for a new problem and some components would be replaced if it is not. According to this methodology, a computer-aided modular fixture design system can be established in future. In the system, three sub-bases would be included. Data base stores many function structures that are assembled by modular components to complete some functions. Knowledge base stores the qualitative knowledge that is required in considering the location of the workpieces. Case base stores previous successful design cases that can be applied to develop a new solution. MOP-based memory technique is applied to organize these complex data, knowledge and case base. A demonstrated example is finally provided in this study to illustrate how this methodology works. This methodology principally focuses on inference process of case evaluation and modification. This is the most important and difficult issue on CBR algorithm. In the evaluation of workpiece locating, geometry recognition play a critical role. Feature recognition is beyond this study and then too detail discussion about that would not be given here. For this reason, the methodology can handle simple geometry workpiece only presently.