
Designing new types of heat-resistant steel components is an important and active research field in material science. It requires detailed knowledge of the inherent steel properties, especially concerning their creep ductility. Highly precise automatic stateof- the-art approaches for such measurements are very expensive and often times invasive. The alternative requires manual work from specialists and is time consuming and unrobust. In this paper, we present a novel approach that uses a photometric scanning system for capturing the geometry of steel specimens, making further measurement extractions possible. In our proposed system, we apply calibration for pan angles that occur during capturing and a robust reassembly for matching two broken specimen pieces to extract the specimen's geometry. We compare our results against µCT scans and found that it deviates by 0.057mm on average distributed over the whole specimen for a small amount of 36 captured images. Additionally, comparisons to manually measured values indicate that our system leads to more robust measurements.
Volker Knauthe, Maurice Kraus, Max von Buelow, Tristan Wirth, Arne Rak, Laurenz Merth, Alexander Erbe, Christian Kontermann, Stefan Guthe, Arjan Kuijper, and Dieter W. Fellner
Session I
Research Line: Computer vision (CV), Defect detection, Industrial quality control, Research Line: Computer graphics (CG), Industrial maintenance, Lead Topic: Digitized Work, Measurements, Applied computing, Industrie 4.0, CCS Concepts: Computing methodologies --> Shape analysis; Applied computing --> Engineering, Computing methodologies, Shape analysis, Engineering
Research Line: Computer vision (CV), Defect detection, Industrial quality control, Research Line: Computer graphics (CG), Industrial maintenance, Lead Topic: Digitized Work, Measurements, Applied computing, Industrie 4.0, CCS Concepts: Computing methodologies --> Shape analysis; Applied computing --> Engineering, Computing methodologies, Shape analysis, Engineering
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