Knowledge in the Domain of Feature-Based Fixturing Planning

Abstract

This paper focuses on the development of domain knowledge in feature-based fixturing planning. A knowledge base can be classified into compiled, qualitative, and quantitative knowledge. The knowledge is expressed in the form of rules to aid the assessment of machining sequences. One example is the utilization of the combination factor for feature primitives. The assessment of the combination factor precludes less optimal machining sequences for machining operations. Four sub-domains, viz., predicate logic, frames, production rules, and procedural programs represent the domain knowledge in fixturing planning. Production rules are the most essential type of knowledge for fixturing planning process. They are a collection of fixturing rules and axioms. The inference mechanism based on the 3-2-1 fixturing method can refer to the production rules and suggest fixturing configurations from a feature-based workpiece data model.