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| 題 名 | 關聯規則應用於智慧工廠生產績效管理之研究=Association Rules Mining for Production Performance Management in AI Manufacturing |
|---|---|
| 作 者 | 林小甘; 廖紫柔; | 書刊名 | 管理資訊計算 |
| 卷 期 | 13:2 2024.09[民113.09] |
| 頁 次 | 頁79-85 |
| 分類號 | 555.6 |
| 關鍵詞 | 智慧工廠; 關聯規則; R-arules套件; 生產績效管理; AI manufacturing; Association rules; Apriori; R-arules package; Production performance management; |
| 語 文 | 中文(Chinese) |
| DOI | 10.6285/MIC.202409_13(2).0006 |
| 中文摘要 | 當今製造系統中人工智慧技術的發展對預測性維護、品質保證和流程優化等智慧工廠應用非常有幫助。關聯規則是一種基於規則的機器學習方法,用於發現大型資料庫中變數之間的關聯性,已廣泛應用於商業智慧應用和智慧製造系統的決策中。然而,針對生產績效管理的預測製造系統的研究很少。本文提出Apriori演算法採用R-arules套件構建,用於預測分析以提高產品品質、生產穩定性和效率。進行了一個案例研究來說明所提出方法的可行性。 |
| 英文摘要 | The development of Artificial Intelligence (AI) technology in today's manufacturing systems is very helpful for AI Manufacturing applications such as predictive maintenance, quality assurance and process optimization. Association rule learning, a rule-based machine learning method for discovering interesting relations between variables in large databases, has been widely used across business intelligent applications and AI Manufacturing for decision-making. However, there are few studies on predictive manufacturing systems for production performance management. This paper proposes Apriori-Based learning in R-arules for providing the predictive analytics to improve product quality with production stability and efficiency. A case study is conducted to illustrate the feasibility of the proposed approach. |
本系統中英文摘要資訊取自各篇刊載內容。