Imaging sensor data modelling and evaluation based on optical composite characteristics: Investigation of data quality for inline inspection

Sebastian Meister, Lars Grundhöfer, Jan Stüve, Roger M. Groves

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Automated Fibre Placement is a common manufacturing technique for composite parts in the aero-space industry. Therefore, a visual part inspection is required which often covers up to 50% of the actual production time. Moreover, the inspection quality of this manual step fluctuates significantly. A camera-based automated inline inspection is capable of increasing the inspection efficiency and accuracy. However, the interpretability of the acquired data strongly depends on the sensor configuration and the inspected material. Thus, this paper introduces methods for modelling and assessing an imaging sensor on the example of a composite material reflecting a spot laser to a camera sensor. In this context, the reflection properties of the material are incorporated into a simulation and validated in comparison to real camera images from the experimental setup. The EMVA 1288 sensor model in combination with the Cramér–Rao lower bound indicates a feasible estimability of the beam propagation, but shows limitations in the predictability of the number of incident photons. The laser spot analysis indicated that the laser spot can deviate from an exact oval shape but its peak value is suitable for robust spot identification in an image. The outlined methodology is also adaptable to other imaging sensors, illumination sources and materials. Thus, the findings can be useful for other fields and manufacturing processes.

Original languageEnglish
Number of pages26
JournalInternational Journal of Advanced Manufacturing Technology
DOIs
Publication statusPublished - 2021

Keywords

  • Automated fibre placement
  • Cramér–Rao lower bound
  • Image quality metrics
  • Inline inspection
  • Laser line scan sensor
  • Sensor modelling

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