Automated Rubber Seal Inspection
Detect defects in complex, flexible seals
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ディープラーニング採用の工業用画像解析向けグラフィカルプログラミング環境
ディープラーニング採用 In-Sight ViDi 画像処理ソフトウェア搭載
Chemical masks, gas masks, and respirators generally consist of a reusable mask with replaceable filters. The flexible, tight seal between filter and mask, as well as that around the face, can be made from a range of elastomers or rubbers, including silicones, polyurethanes, and butyl rubbers.
Elastomeric mask seals are manufactured through injection molding, transfer molding, or compression molding, depending on material and use. Defects will compromise the seal or limit part life and must be detected before final assembly.
The complex folds, flexibility, and often dark surfaces of such seals make it difficult for conventional machine vision to detect defects and distinguish good parts from bad. The mask manufacturer receiving these elastomeric parts will reject ones that do not meet the standard, or assembled masks will show failures in use, sometimes with serious liability issues.
Cognex Deep Learning automates rubber seal defect detection quickly and effectively. The defect detection tool trains on a small set of images of the full range of good rubber or elastomeric seals. Given their flexibility, seals may flop and sag in various unpredictable ways when presented for visual inspection, presenting a wide range of appearances. Cognex Deep Learning incorporates this wide variability of good parts, and so accurately detects anomalies that are outside of the acceptable range while passing all functional seals.
