基於RGB-D影像之粉體塗裝生產線即時工件辨識的研究

Study of Real-time Workpieces Recognition in Powder Coating Production Line Based on RGB-D Images

洪承孝(CHENG-HSIAO HUNG)

指導教授 ： 廖珗洲

朝陽科技大學/資訊學院/資訊工程系/碩士(2019年)

摘要

粉體塗裝的應用非常廣泛，主要針對金屬或鋁合金表面的保護或美觀，如:辦公家具、自行車架等。然而，一條粉體塗裝生產線可能長達400公尺，加工件完成粉體塗裝所需時間約1個小時，需要經過多個步驟：包含：清潔、表面調整、沖洗/烘乾、靜電噴房、烘烤等，如何即時掌握整個塗裝生產線的運作狀況是製造執行系統(MES)很重要的一環，也是未來提升到智慧製造時必須要先掌握的資訊。有鑑於此，本研究的產學合作企業-明昌工業曾經在二年前導入RFID射頻標籤的解決方案，不過面臨幾個問題，包含：成本問題、一勾多料與一料多勾、工件遺失與重複施工等，導致無法順利導入報工系統，因此本研究期望運用影像辨識技術來達成上述目標。
本研究中預計在生產線上建置多個監測站，由於粉體塗裝是採分批的方式來進行，同一批加工件從外觀來說都是一樣的，所以每個站都會進行偵測、分群與計算數量的功能，進一步地，為了讓生產線上所建置的多個監測站可以分辨這些加工件的群組，每個站會設立一個同步的硬體計數器，透過計數器的數值就可以達成群組分辨、遺失與重複施工的檢出。經過實驗分析，不論在白天或晚上的分群平均正確率可達到90%以上，生產線停線判斷的正確率，白天達90%以上，晚上則達到100%，這些結果顯示本研究提出的分群方法具有一定的實用性。

關鍵字

粉體塗裝 ； 圖形識別 ； 電腦視覺 ； 深度影像 ； 前景物體偵測

並列摘要

Powder coating is used often on the metal or aluminum for protective as well as decorative purposes, for example, office furniture, bicycle frame, and so on. However, the length of a powder coating production line may be over 400 meters. The elapsed time to finish the powder coating of a workpiece is about one hour. There are several steps in the powder coating process, including cleaning, pre-treatment, rinse/dry, powder coat, curing. So, the tracking and tracing workpieces in the production line and collecting real-time production data is an important issue of manufacturing execution system (MES). It is also the key information of intelligent manufacturing. In order to achieve the above goal, the cooperative company, MaChan International Co., LTD., attempted to develop a RFID-based system two years ago. However, several problems cause the failure of the system, including, the cost too high, one hook with multiple workpieces, multiple hooks with one workpiece, lost workpiece, duplicate process. This study is proposed and expected to achieve the above goal using pattern recognition technique.
In this study, several monitoring stations will be installed in the production line. All the workpieces are coating in groups. The workpieces in the same group are almost identical. For every station, those workpieces are detected, grouping, and counting. In advanced, a synchronized hardware counter is used in every monitoring stations. The counter value can be used to identify the same group, lost workpiece, or duplicate processed workpieces. In the experimental study, the accuracy of the group identification can reach 90% no matter in daytime or nighttime. The accuracy of the line stop detection can reach 90% in daytime and 100% in nighttime. The above results should that the proposed group identification method is feasible.

並列關鍵字

powder coating ； pattern recognition ； computer vision ； depth image ； foreground object detection

參考文獻

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