| 摘要: | 本論文透過智慧製造概念結合微流體製程方法所需要的參數，並同時考量到製造產量、成本與時間來達到流程依據的判別的方法，來達到微流體商業化的目的。
我們根據前端材料解析度、粗糙度、成本、產量，分別設定的25μm、50μm、40K、5min與後端結合強度、結合時間設定的20mpa、5min進行區分模組判別式流向，流程會根據輸入理想的條件值以流向最終的製造方法，並實際提取案例以實際驗證預測流向正確性，最後將預測結果方法轉換成有效的ID值並實際在CIROS軟體中進行工廠內產線製作流程，此工廠可根據載盤依序輸送至加工站進行訊號判別，透過機台設定的訊號辨別進行加工，並使每個加工站皆能自主完成作業，機械手臂的操作能準確的定位抓取材料與位置，以實踐智慧化工廠內少量多樣性產品的概念驗證，透過取代人為操作使工廠的效率能得到明顯的提升，過程中人力只需達到監督的作業以監視產品質量。
;This thesis proposes concept of smart manufacturing is combined with the parameters required by the microfluidic process method, and at the same time, the manufacturing yield, cost and time are considered to achieve the process-based identification method to achieve the purpose of microfluidic commercialization.
According to the front-end material resolution , roughness , cost , and output , we set 25μm , 50μm , 40K , 5min and the back end bonding strength , and the bonding time set 20mpa , 5min to distinguish the module discriminant flow direction , and the process will be based on the input ideal The condition value of the flow to the final manufacturing method , and the actual case is extracted to actually verify the correctness of the predicted flow direction .Finally ,the predicted result method is converted into a valid ID value and the production lines production process of the factory is actually carried out in the CIROS software . This factory can According to the trays are sequentially transported to the processing station for signal identification, processing is performed through the signal identification set by the machine, and each processing station can complete the work autonomously, and the operation of the robotic arm can accurately locate and grasp the material and position. Practicing the proof of concept of a small number of diverse products of the smart factory, the efficiency of the factory can be significantly improved by replacing the manual operation, and the manpower in the process only needs to be supervised to monitor the product quality. |
