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題名：整合性組裝順序規劃之KBE系統
作者：戴培豪
作者(外文)：TAI, PEI-HAO
校院名稱：中華大學
系所名稱：科技管理學系(所)
指導教授：陳文欽
謝玲芬
學位類別：博士
出版日期：2009
主題關鍵詞：組裝規劃；組裝優先順序圖；類神經網路；實驗設計；田口方法；assembly planning；assembly precedence diagrams；neural networks；design of experiment；Taguchi method
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產品組裝規劃研究一般可分成圖形導向、知識導向及人工智慧等三類，而其三種研究的各自缺點在於圖形導向需耗費大量時間，知識導向很困難去發掘最佳解，人工智慧需要高度計算效率。為了克服上述問題，本研究發展一個整合三階段圖形導向啟發式作業規則，並以田口及實驗設計建構穩健倒傳遞神經網路預測器與智慧型物件導向的知識工程(KBE)系統。
首先協助工程師依據產品上位圖規則去產生一正確CAD爆炸圖，並依據圖形導向啟發式作業規則，建構關係模型圖及組裝優先順序圖，進而產生圖形解之最佳組裝順序-當作倒傳遞類神經網路學習訓練樣本；再則透過倒傳遞類神經網路穩健預測器及物件導向的KBE系統，產生可行的組裝順序。本研究所建立之組裝順序規劃KBE系統，係利用Siemens NX/KF二次開發軟體模組與結合倒傳遞神經網路預測器，預測並獲得較佳的機構件或塑膠件產品的組裝順序；其中在使用者UI介面可獲取CAD系統組合件的體積、重量、特徵數量等資訊，並輸入相關參數(接觸關係值與總懲罰值)，以倒傳遞類神經網路訓練樣本，建構穩健的組裝順序預測引擎，預測可行的組裝順序；本系統不但可顯示其爆炸圖及動態組裝模擬，並可將所有組裝資訊儲存，建立完善的知識庫。
最後應用三種實體樣品在組裝順序規劃KBE系統上，以玩具模型車當為學習(訓練)樣本，玩具摩托車模型與無刷直流風扇當為測試樣本，去評估及驗證上述方法的可行性。結果顯示所提的研究模式，可有效率地建立穩健倒傳遞神經網路引擎及快速預測可行的組裝順序，並讓R&D設計者深入了解組裝接觸關係、組裝困難度與虛擬3D實體組裝限制因素。
Research in assembly planning can be categorized into three types of approach: graph-based, knowledge-based and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; the second approach is difficult to find the optimal solution; and the third approach requires a high computing efficiency.
To tackle these problems, this study develops a three-stage approach (i.e., firstly create a correct CAD-oriented explosion graph and then find a graph-based assembly sequence using Above graph, relational model graph and assembly precedence diagram; at last, generating a feasible assembly sequence) integrated with robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence for mechanical or plastic products. The research focuses on building a novel KBE system for assembly sequence planning (ASP), which joins BPNN predictor and Siemens NX/KF second development module together to create feasible assembly sequences. System user can easily access the volume, weight and feature number through Unigraphics NX system interface, and input the related parameters such as contact relationship number and total penalty value, and predict the feasible assembly sequence via a robust BPNN engine. Furthermore, the proposed system can demonstrate the explosion views and vivid assembly simulations, save the entire assembly information, and setup a consolidate knowledge base.
Finally, three real-world examples- the toy car model as a learning (training) sample, toy motorbike model and a brushless DC fan as verifying (testing) samples, are dedicated to evaluating the feasibility of the proposed KBE system in terms of the differences in assembly sequences. The results show that the proposed model can efficiently generate robust BPNN engines, facilitate feasible assembly sequences and allow the designers to recognize the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type.
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