The Process Parameter Recommender System Of High-Performance Machining For Difficult-to-Cut Materials Based On Ontology

Currently, difficult-to-cut Materials have been widely applied to aeronautics and astronautics,shipbuilding, nuclear energy, weaponry industry and other areas, However, due to complexity,diversity, empiricity and uncertainty of difficult-to-cut materials process knowledge, For a long time,how to improve management and effective reasoning, and then how to efficient, economic andhigh-quality processing becomes one of the important issues in the modern manufacturing fields.Therefore, the paper will introduce ontology for process parameter Recommender system, whichfocuses on the research into ontology fuzzy reasoning algorithm and the establishment of domainontology knowledge base and rule base. The main researches are as follows:(1) A system framework base on ontology is presented, and designs the system operation processand discusses its key techniques. The framework includes user layer, application layer, reasoning layer,construction layer of knowledge base and rule base based on ontology, knowledge source layer, inwhich construction layer of knowledge base and rule base based on ontology and knowledge sourcelayer are the core layer of the system.(2) Based on the ontology technology, references the typical ontology construction method andaim at the characteristics of difficult-to-cut materials domain knowledge, extracts process knowledge,represents process knowledge using the owl ontology description language, and then constructsontology class and hierarchical structure; and constructs uncertainty rule base using f-SWRL extentedwith weight and credibility by SWRL and gives a method of ontology knowledge base consistencycheck and rule base redundancy processing.(3) A kind of uncertainty rule-base reasoning algorithm based on ontology is designed; andpretreatment user input conditions and converts the formation of ontology knowledge base anduncertainty rule base, and then loads the result into Jess for fuzzy reasoning and extends reasoningresults using semantic similarity method.(4)At last, Combines with the application background of an aviation factory research project, andgives the system implementation and main running effect of difficult-to-cut materials processparameter recommender system based ontology.