三維零件相似性計算為設計再利用的關鍵技術，以往研究將三維模型轉化成Shape Signature後，再根據特定的相似度定義進行計算，其結果受限於單一Shape Signature形狀辦識的限制。此外多數文獻著重於完整三維零件的相似度，尚未提出部分相似的搜尋方法，並多以幾何資訊作為計算的依據，未能充分考量人類的認知結果，不能符合實際應用的需求。有鑑於此，本論文提出四個全新的以特徵為基之整合性搜尋機制，首先整合多層次(Level of detail, LOD)的概念，以提供完整/部分相似性的搜尋機制。接著結合特徵辨認與D2分佈圖，克服原本D2分佈圖鑑別能力的不足，提供更精確的比對結果。透過 PDM與CAD的整合，以多Shape Signature建立三維零件搜尋引擎。最後，則將特徵相似因子整合至比對方法中，提供符合人類相似性認知的搜尋方法。本論文亦討論各方法的性質，並比較其優缺點。未來可將所提架構延伸至組立件產品，若能以三維零件的內容為檢索條件，可使搜尋方法更加多元化。本研究著重於設計階段的應用，但亦可推廣至產品生命週期中其他活動之相似性應用。

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