在 PCB 產品實現流程中，前後製程間具有高度依存關係，其生產節拍也因此受到前後生產料件品質變異、設備穩定、技術參數等製造因素的制約。生產過程中資訊無法即時掌握，當有異常狀況發生未能即時發現時，更造成重大報廢損失。因此，跨領域整合各技術優點，開發一個更有效率、更具智慧的製造系統，以高效率可靠的方式擷取製造設備的資料；提前偵測潛在的異常，以提供製程管制之決策，為當務之急。本論文由智慧製造的觀點，提出一個架構在先進製程控制與數位分身概念為基礎，以 PCB 高密度互連技術之關鍵製程為研究對象。建構雷射鑽孔機的“數位分身”，透過批次控制模型與機器學習演算法使得設備具備自我預測與監控能力之新功能。經實際應用在雷射鑽製程進行離線模擬實驗，結果顯示: 即時監控上工程之前饋數據、機台參數與預測產品品質特性值, 達成線上實時檢查之目標，藉以避免因為量測或檢查延遲而造成機台等待或品質失效。

In the PCB process, the process between the upstream and the downstream is highly dependency, the production pace is constrained by manufacturing factors such as product quality variation, equipment stability, and technical parameters. When there is an abnormal situation and information in the production process that cannot be
immediately discovered, it will cause significant loss of scrap. Therefore,
cross-domain integration of various technical advantages to develop a more efficient
and intelligent manufacturing system, the efficient and reliable way to capture the data form manufacturing equipment; detect potential anomalies in advance to provide
process control decisions.
This study is based on smart manufacturing, and proposes an architecture that
integrate the concepts of Advanced Process Control (APC) and Digital Twin (DT) on
the key process of PCB. The “Digital Twin” of the laser drilling machine is built, and has the new function of self-predicting and monitoring capability through batch control (R2R) model and machine learning algorithm. After the actual application in the laser drilling process for offline simulation experiments, the results show: Real time monitoring the feed forward data and machine parameters and predict product quality characteristics to achieve the goal of online inspection, and avoid machine waiting or quality failure due to measurement or inspection delay.

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