Automated Defect Analysis using Optical Sensing and Explainable Artificial Intelligence for Fibre Layup Processes in Composite Manufacturing

S. Meister*

*Corresponding author for this work

Research output: ThesisDissertation (TU Delft)

79 Downloads (Pure)

Abstract

In modern aircraft, structural lightweight composite components are increasingly
used. To manufacture these components in a costeffective way, robust production systems for the manufacturing of complex fibre composite components are necessary. A rather novel but already established process for fibre material deposition is the Automated Fibre Placement (AFP) technology, which automatically places several narrow, parallel fibre tows on a mould. Typically, a component consists of several, often hundreds of stacked layers of these fibre material strips. However, when these narrow fibre tows are placed in position, layup defects can occur and reduce the mechanical properties of the component. Hence, in safety critical applications, such as aircraft manufacturing, a visual inspection of every single ply is mandatory. This inspection step is currently carried out by an expert via a visual examination, which requires up to 50 % of the total production time. An automation of this inspection process using suitable algorithms offers great potential for increasing process efficiency. However, with the growing complexity of these respective defect analysis algorithms, their performance potentially increases, but the comprehensibility of the machine decision and the behaviour of the algorithm become more challenging. This is problematic especially in safety critical applications. In addition, the data quality of recorded images is influenced by the very matte, low reflective Carbon Fibre Reinforced Plastic (CFRP) material which raises the uncertainty of an inspection....
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Groves, R.M., Supervisor
  • Stueve, J., Advisor
Thesis sponsors
Award date22 Mar 2022
Print ISBNs978-3-00-071580-8
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Inspection
  • Automated Fiber Placement
  • Laser Line Scan Sensor
  • Machine Learning
  • Explainable Artificial Intelligence
  • Sensor Modelling
  • Computer Vision

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