Bayesian optimization-based prediction of the thermal properties from fatigue test IR imaging of composite coupons

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008781
URN to cite this document: urn:nbn:de:bvb:703-epub-8781-9

Title data

Demleitner, Martin ; Albuquerque, Rodrigo Q. ; Sarhadi, Ali ; Ruckdäschel, Holger ; Eder, Martin A.:
Bayesian optimization-based prediction of the thermal properties from fatigue test IR imaging of composite coupons.
In: Composites Science and Technology. Vol. 248 (2024) . - 110439.
ISSN 1879-1050
DOI der Verlagsversion: https://doi.org/10.1016/j.compscitech.2024.110439

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Abstract

The prediction of the prevailing self-heat transfer parameters of a glass/epoxy composite coupon during fatigue testing in general and the distinction between viscoelastic- and frictional crack growth-related energy dissipation in particular, are not trivial problems. This work investigates the feasibility of predicting the convective film coefficient, the total work loss as well as the ratio between viscoelastic and fracture-induced damping from thermal images using Bayesian optimization in conjunction with 3D FE thermal analysis. To this end, glass fiber/epoxy biax coupons are pre-damaged by means of a drop weight impact machine and subsequently tested under uniaxial tension-tension high cycle fatigue conditions. IR images are taken of the self-heating thermal profile at steady-state conditions. Synthetic surface thermal images are generated by numerical thermal analysis of the damage distribution obtained by μ-CT scanning prior to testing. Bayesian optimization of the aforementioned parameters is conducted by minimizing the loss function between the as-measured and the synthetic IR image. The predicted work-loss is consequently validated against the measured hysteretic response, from which a very good agreement is found.

Further data

Item Type:	Article in a journal
Keywords:	Bayesian optimization, Glass/epoxy composites fatigue testing, Thermal properties, IR imaging
DDC Subjects:	600 Technology, medicine, applied sciences > 620 Engineering 600 Technology, medicine, applied sciences > 670 Manufacturing
Institutions of the University:	Faculties > Faculty of Engineering Science > Chair Polymer Materials > Chair Polymer Materials - Univ.-Prof. Dr.-Ing. Holger Ruckdäschel Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Polymer Materials
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-8781-9
Date Deposited:	19 Dec 2025 10:46
Last Modified:	19 Dec 2025 10:47
URI:	https://epub.uni-bayreuth.de/id/eprint/8781

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