在全球化的浪潮下，工業產品的生命週期變得越來越短，特別是在電子產業。新產品上市的時間已成為一個公司拓展市佔率的核心能力。為了縮短產品設計到批量生產的時間，工程師必須經常在有限資訊的不確定條件下作出決定。雖然機器學習方法可以幫助尋找有用的資訊，但欲建立穩健的預測模式時，其對於所需的最少訓練樣本數是必要的，且由於在產品製造初期並沒有足夠的資料，因此其所建構之模式往往是較不可信賴的。本研究引用Li等人於2009年所提出之整體趨勢擴散技術與其所應用之多層陶瓷電容器案例，以可能性評估過程修訂其虛擬值產生方式，使用M5'模式樹建構一個準確的模型，並以樹狀結構具體呈現所學習到的知識，用以協助批量生產前縮短交貨時間。試驗結果顯示，在試產期間使用少量有限的資料去快速開發一個生產模式是可行的。

Product life cycles are becoming shorter and shorter owing to the increasing pressure of global competition. However, companies may dominate their target market if they can shorten their production cycle time to accelerate their new products coming to market. While trying to shorten the cycle time from product design to mass production, firms often need to make decisions with limited data at each early developmental stage. Thus although machine learning algorithms can help derive meaningful information for decision makers, the number of training samples is always a key factor in determining their knowledge acquisition capability. This research develops a Possibility-Based Surviving Procedure to Generate Virtual Samples to Solve the Small-Data-Learning Problem in pilot run stage and takes the multi-layer ceramic capacitor case (Li et al., 2009) as an example to figure out a precise model which concretely represents the learned knowledge of the process to help shorten the lead-time before mass production. The results reveal that it is possible to rapidly develop a model of production with limited data from pilot runs.

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