A Novel Approach for Mining Machining Process Decision Knowledge Based on Knowledge Constraint Combined with Water Wave Optimization Algorithm

Knowledge discovery constitutes a vital component in building intelligent CAPP systems, and the effective discovery of process knowledge has become a prominent research focus within intelligent manufacturing. Process decision knowledge is a type of knowledge that reflects the relations between process data items, represented in the form of production rules. However, PDK discovery faces low accuracy challenges from complex high-dimensional manufacturing data and implicit experience-dependent process decisions. This paper proposed a PDK mining framework that combines knowledge constraint and the water wave optimization algorithm. This approach formulated prior knowledge mathematically using an association discriminant matrix and embedded this representation into the knowledge mining model, thus equipping the algorithmic framework with the ability to discover PDK accurately. The WWO is utilized to search within the sample space for combinations of process data items that constitute valid knowledge. In contrast to traditional association rule mining algorithms that lack accuracy and template-based methods that are inherently rigid, the proposed approach provides a robust solution by achieving over 90% correctness in PDK mining. It also serves as a demonstration and offers insights for mining similar rule-based knowledge in other fields.