銑削加工廠商的加工流程中，關鍵的決策步驟是銑床的選擇。而在選擇最合適的銑床過程中，成本、品質與加工速度三項指標相互影響且密不可分。
　　目前我國之銑削加工廠商，絕大多數仍透過決策者之專業知識進行人為的銑床選擇、成本估算等決策，也缺乏一套能夠有效管理加工資訊的資料庫系統。這些問題容易造成人力與時間開銷增加，缺乏量化的參數的輔助也形成導入智能化過程的阻礙。
　　為了解決上述問題，本研究設計銑床選擇問題的完整評估流程，並開發銑床推薦系統。透過「銑床篩選模組」快速篩選出符合加工條件之候選銑床，經由「工件成本估算模組」提供量化的估價模式，並以層級分析法(Analytic Hierarchy Process, AHP)為基礎設計「銑床評估模組」，解決銑床效益評估的問題。
　　同時導入機器學習概念，根據案例式推理法(Case-Based Reasoning, CBR)設計「銑床推薦模組」，提供使用者透過簡易且高彈性的參數與權重配置，對過往舊案例進行監督式學習，推薦出新案例適合的機台，模組中的十褶交叉驗證方法則作為驗證推薦準確率之依據。並且以「銑削加工資料庫」作為加工資訊管理的解決方案。
　　通過模組效能測試、案例分析與導入前後比較顯示，本研究之系統可作為銑床選擇問題的解決方案。

SUMMARY
Machine selection is a critical decision-making step in manufacturing processes of the milling industry while production cost, quality and delivery time are inseparable and correlative key factors during such procedures. At present, most of the milling sectors still rely heavily on professionals in deciding machine selection and estimating their production cost. There is also a lack of database systems to effectively manage information. These problems may easily lead to increased labor costs and time waste. Moreover, the shortage of quantitative parameters also forms an obstacle in the importation of the intelligent manufacturing systems.
In order to solve the above problems, this study designs a complete evaluation process for milling machine selection and develops a milling machine recommendation system. Through the “Milling Machine Screening Module,” the candidate milling machines that satisfy the processing restrictions can be quickly screened. The quantitative cost evaluation model is provided through the “Cost Estimating Module.” Based on the Analytic Hierarchy Process (AHP), the “Milling Machine Evaluating Module” is designed to solve the problem of benefit evaluation.
At the same time, this system introduces the concept of machine learning and designs "Milling Machine Recommendation Module," which is based on Case-Based Reasoning (CBR) to provide users with a recommendation of a suitable milling machine for a new case. The supervised learning is based on past cases through a simple and flexible parameter and weight configuration. The ten-fold cross-validation method in the module serves as the basis for verifying the accuracy of the recommendation, and “Processing Database” as the information management solution.
After a performance test, case analysis and a before-and-after comparison, findings show that the system of this study can be used as a solution to the problem of milling machine selection.

Keywords: Machine selection, Milling, Recommendation system, Machine learning, Case-Based Reasoning.

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