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Characterization of process-induced defects in automated fiber placement manufacturing of composites using fiber Bragg grating sensors

Author(s):



Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 1, v. 17
Page(s): 108-117
DOI: 10.1177/1475921716685935
Abstract:

With the increasing use of automated fiber placement method for manufacturing highly precise bespoke composite components in the aerospace industry, the level of manufacturing defects within the laminate structure needs to be monitored and minimized for structural integrity. One of the main common defects in automated fiber placement process is misalignment between the tape paths in successive courses which leads to non-integrity of laminate and consequently significant reduction in mechanical strength of the laminate. Therefore, it is necessary to find an appropriate inspection method to monitor and identify these processing defects at the earlier stages of manufacturing. Since optical fiber Bragg grating sensors are being increasingly utilized for structural health monitoring in composite materials and as they were successfully implemented by Oromiehie et al. in their earlier work for on-line lay-up process monitoring, the same methodology is once again tried for identifying the misalignment defects in automated fiber placement process. The experiments are carried out on glass-fiber/nylon laminate with embedded fiber Bragg gratings for the automated tape placement method. The defects due to misalignment are identified by the fiber Bragg grating sensors through their reflected wavelength changes during the automated manufacturing process. The analysis of results indicates that the fiber Bragg grating sensors can be reliably implemented for on-line defect monitoring during the automated fiber placement process to ensure the quality of final product and maintain the expected design life.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921716685935.
  • About this
    data sheet
  • Reference-ID
    10562052
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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