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Multi-sensor Data Fusion for In-line Visual Inspection

Interview on practical experiences
: Pierer, Alexander; Wiener, Thomas; Gjakova, Leutrim; Barthel, Sven; Naumann, Martin; Mende, Mattias; Hoffmann, Michael; Weise, Dieter; Koziorek, Jiri; Bilik, Petr; Groth, Amy

Volltext urn:nbn:de:0011-n-6402118 (832 KByte PDF)
MD5 Fingerprint: 482b2b1bc9b2cc3342266a46d580c914
Erstellt am: 8.9.2021

Chemnitz: Fraunhofer IWU, 2020, 10 S.
European Commission EC
H2020; 856670; GeoUS
Geothermal Energy in Special Underground Structures
Bericht, Elektronische Publikation
Fraunhofer IWU ()
data fusion; automated optical inspection; machine learning; image processing; parallelization

Visual inspection is the cornerstone of most quality control workflows. When performed by humans the process is expensive, prone to error, and inefficient: a 10%-20% pseudo scrap and slippage rate and production bottlenecks are not uncommon. Under the name IQZeProd (Inline Quality control for Zero-error Products), researchers at Fraunhofer IWU are developing new, inline monitoring solutions to recognize defects as early in the production process as possible for a variety of materials such as wood, plastics, metals, and painted surfaces. The system uses multi-sensor data fusion from a variety of sensors to recognize structural and surface defects as the components travel the production line. The goal is to make industrial manufacturing processes more robust and sustainable by increasing process reliability and improving defect detection. At the heart of the system is the researchers' own Xeidana® software framework and a matrix of twenty industrial cameras. The researchers had very specific camera criteria: global-shutter monochrome sensor; low-jitter real-time triggering; reliable data transmission at very high data rates and straightforward integration into their software framework.