臺灣博碩士論文加值系統

機台保修所需的知識必須隨設備儲存且即時更新，並可於服務流程中主動提示保修人員所需之操作知識。傳統上，保修人員須先具備相當背景知識與類似經驗，再依據手冊指示來想像工作內容。如此的工作方式隨著客製化產品的多樣性及經驗時間的拉長，將對工作人員形成極大的智能負擔，必定影響工作品質。本研究應用本體架構以相鄰關係為基礎，建構機器結構之零組件關聯拓樸，並於關聯上附記多項互動屬性，藉此描述機構之靜態本質與動態質變。靜態本質可用來推理產出保修拆裝知識，而動態質變則可因機構之關聯類別，未來隨著機構的使用歷程而記錄且調整性質的異動，使能即時推算並產生保修所需之知識，由系統主動依據最新的機構性能狀態提供保修程序，不再是以工作時數來模糊認定機台性能，才再由人員閱讀手冊來推測保修程序。

The knowledge for machine maintenance should be kept with the facility and updated along with the service lifecycle. In this way, the knowledge can be the most up-to-date information for generating service instructions. In the past, service providers are required to possess full background and experiences, plus feasible extensions of the manual instructions. However, the skill is no longer beneficial for customization may create disjointed similarity and experiences. In this research, an ontology-based method is proposed to express the adjacency attributes among the parts. Both static or innate characters and dynamic property changes can be recorded. The innate characters are to derive detail steps and tools for assembly/disassembly operations, and the dynamic changes may be composed to indicate in-depth knowledge for the operations. In overall, the maintenance system will actively create timely detailed instructions to the service provider, rather than doing periodic maintenance with manual hints.

摘要...i
Abstract...ii
誌謝...iii
目錄...iv
表目錄...vi
圖目錄...vii
第一章 緒論...1
1.1研究背景...1
1.2研究動機...1
1.3研究目的...2
1.4 研究限制...3
1.5 論文章節說明...3
第二章 文獻探討...4
2.1知識管理...4
2.1.1知識的特性...4
2.1.2知識的應用...4
2.2知識架構...4
2.3本體論介紹...5
2.3.1本體論基本架構...5
2.3.2本體建立方法...6
2.3.3本體論相關應用...7
2.4智慧製造...8
2.5 文獻總結...9
第三章 研究方法...10
3.1方法概述...10
3.2屬性定義...11
3.2.1相鄰關係...12
3.2.2關聯規則定義及判斷...15
3.3本體塑模...18
3.3.1機構組件分類...18
3.3.2本體第一層零件關係層(The relation of part Layer)...19
3.3.3本體第二層零件層(Part Layer)...28
3.3.4本體第三層功能層(Function Layer)...30
3.4自由度...31
3.4.1拆裝自由度...31
3.4.2運動自由度...32
3.5服務歷程階段化...33
3.6服務資料庫...35
第四章 實作部分...36
4.1實作環境...36
4.2動態文件介紹...36
4.3範例本體三層架構...37
4.4木材切削部分拆解示範...42
第五章 結論與未來研究...55
5.1結論...55
5.2未來展望...55
參考文獻...57
Extended Abstract...59

Ali, M. M., Rai, R., Otte, J. N., & Smith, B., “A product life cycle ontology for additive manufacturing.”, Vol. 105, pp. 191-203, 2019.
Chai, X., “Diagnosis Method of Thyroid Disease Combining Knowledge Graph and Deep Learning.”, Vol. 8, pp. 149787-149795, 2020.
Drucker, P. F., “Post-Capitalist Society.” Oxford: Butterworth Heinemann. pp. 40, 18, 40–41, 88, 97–98, 99, 51, 53, 1993.
Gruber, T. R., “A translation approach to portable ontology specifications.”, Knowledge acquisition, 5(2), 199-220, 1993.
He, Y., Hao, C., Wang, Y., Li, Y., Wang, Y., Huang, L., & Tian, X., “An ontology-based method of knowledge modelling for remanufacturing process planning.”, Vol. 258, 2020.
Huang, C. C., and Liang, W. Y., “Explication and sharing of design knowledge through a novel product design approach.” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 36(3), 426-438, 2006.
Ikujiro, N., and Takeuchi, H., “The Knowledge-creating Company: How Japanese Companies Create the Dynamics of Innovation”, Oxford University Press, 1995.
Khobreh, M., Ansari, F., Fathi, M., Vas, R., Mol, S. T., Berkers, H. A., & Varga, K., “An Ontology-Based Approach for the Semantic Representation of Job Knowledge.” in IEEE Transactions on Emerging Topics in Computing, Vol. 4, No. 3, pp. 462-473, 2016.
Liu H. C., Luan, X., Li, Z. & Wu, J., “Linguistic Petri Nets Based on Cloud Model Theory for Knowledge Representation and Reasoning.” in IEEE Transactions on Knowledge and Data Engineering, Vol. 30, No. 4, pp. 717-728, 2018.
Qin, Y., Lu, W., Qi, Q., Liu, X., Huang, M., Scott, P. J., & Jiang, X., “Towards an ontology-supported case-based reasoning approach for computer-aided tolerance specification”, Knowledge-Based Systems, 141, 129-147, 2018.
Shi, Y., Han, Q., Shen, W., & Zhang, H., “Potential applications of 5G communication technologies in collaborative intelligent manufacturing.”, Vol. 1, Issue 4, pp. 109 – 116, 2019.
Tao, W., Lai, Z. H., Leu, M. C., Yin, Z., & Qin, R., “A self-aware and active-guiding training & assistant system for worker-centered intelligent manufacturing.”, Vol. 21, pp. 45-49, 2019.
Uschold, M., & Gruninger, M., “Ontologies: principles, methods and applications.”, Knowledge engineering review, 11(2), 93-136, 1996.
Wan, N., Li, L., Ye, C., & Wang, B., “Risk Assessment in Intelligent Manufacturing Process: A Case Study of an Optical Cable Automatic Arranging Robot.”, in IEEE Access, Vol. 7, pp. 105892-105901, 2019.
Welty, C., & Guarino, N., “Supporting ontological analysis of taxonomic relationships.”, Data & Knowledge Engineering, 39(1), 51-74, 2001.
Zhou, A., Yu, D., Zhang, W., “A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA.”, Vol 29, Issue 1, pp. 115-125, 2015.