本研究提出知識可擴充之本體論專家系統的建構方法，並以冷鍛輪緣螺帽製程及模具設計作為此方法之範例。此方法主要可區分為兩個重要部分，第一部分係利用Protégé軟體將系統性整理後的領域知識以本體論及語意網路規則語言的形式表達用以建構知識庫，接著使用由Jess規則引擎提供的函式庫於Java程式語言中開發及推論。第二部分則是結合製程模擬與田口實驗方法系統性地產生模具之最佳化參數組合，即產生新的領域知識，而後新的領域知識利用前述的方法可被輸入本體論專家系統的知識庫，也就是說若未來有任何新產生或需修改的領域知識，知識庫可被擴充及更新。本文建構之系統在輸入必要的資訊如產品公稱及產品規格後，即能推論出可使用的鍛造成形法及模具設計，使用者可將推論的結果作為決策支援，有效縮短模具設計時間。

This study proposes a method of constructing an ontology-based expert system with an expandable knowledge base. The cold forging process and tool design of flange nuts is taken as an example to demonstrate the method. There are two essential parts of this method. In the first part, the software Protégé is used to express experiential knowledge as a Web Ontology Language (OWL) ontology and Semantic Web Rule Language (SWRL) rules. The inference is executed in Java by means of the API provided by Jess, the rule engine for the Java platform. In the second part, new knowledge is acquired from parameter optimization experiments, combing process simulation and Taguchi methods. The new knowledge is represented as an OWL ontology and SWRL rules, expanding the knowledge base of the system. The knowledge base can thus be expanded if there are any modifications or new knowledge in the future. The system is capable of inferring a workable forging process and recommended dimensions of tools after the necessary information, such as the nominal size and specifications of the product, has been input. Users are able to reduce tool design time by taking the results from the system as a decision support.

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