TY - CONF A1 - Götschel, S. A1 - Maierhofer, Christiane A1 - Müller, Jan P. A1 - Rothbart, Nick A1 - Weiser, M. T1 - Quantitative defect reconstruction in active thermography for fiber-reinforced composites T2 - 19th World Conference on Non-Destructive Testing 2016 N2 - Carbon-fiber reinforced composites are becoming more and more important in the production of light-weight structures, e.g., in the automotive and aerospace industry. Thermography is often used for non-destructive testing of These products, especially to detect delaminations between different layers of the composite. In this presentation, we aim at methods for defect reconstruction from thermographic measurements of such carbon-fiber reinforced composites. The reconstruction results shall not only allow to locate defects, but also give a quantitative characterization of the defect properties. We discuss the simulation of the measurement process using finite element methods, as well as the experimental validation on flat bottom holes. Especially in pulse thermography, thin boundary layers with steep temperature gradients occurring at the heated surface need to be resolved. Here we use the combination of a 1D analytical solution combined with numerical solution of the remaining defect equation. We use the simulations to identify material Parameters from the measurements. Finally, fast heuristics for reconstructing defect geometries are applied to the acquired data, and compared for their accuracy and utility in detecting different defects like back surface defects or delaminations. T2 - 19th World Conference on Non-Destructive Testing 2016 CY - München, Germany DA - 13.06.2016 KW - Active thermography KW - Numerical simulation KW - Time-resolved thermogram KW - Thermal wave PY - 2016 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-366644 UR - https://www.wcndt2016.com/Programme/show/Th-4-C-4 VL - 2016 SP - Th.4.C.4-1 EP - Th.4.C.4-10 AN - OPUS4-36664 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Müller, Jan P. A1 - Götschel, S. A1 - Maierhofer, Christiane A1 - Weiser, M. T1 - Determining the Material Parameters for the Reconstruction of Defects in Carbon Fiber Reinforced Polymers from Data Measured by Flash Thermography JF - AIP Conference Proceedings N2 - Flash thermography is a fast and reliable non-destructive testing method for the investigation of defects in carbon fiber reinforced polymer (CFRP) materials. In this paper numerical simulations of transient thermography data are presented, calculated for a quasi-isotropic flat bottom hole sample. They are compared to experimental data. These simulations are one important step towards the quantitative reconstruction of a flaw by assessing thermographic data. The applied numerical model is based on the finite-element method, extended by a semi-analytical treatment of the boundary of the sample, which is heated by the flash light. A crucial part for a reliable numerical model is the prior determination of the material parameters of the specimen as well as of the experimental parameters of the set-up. The material parameters in plane and in depth diffusivity are measured using laser line excitation. In addition, the absorption and heat transfer process of the first layers is investigated using an IR microscopic lens. The performance of the two distinct components of CFRP during heating – epoxy resin and carbon fibers – is examined. Finally, the material parameters are optimized by variation and comparison of the simulation results to the experimental data. The optimized parameters are compared to the measured ones and further methods to ensure precise material parameter measurements are discussed. T2 - 43rd Review of Progress in Quantitative Nondestructive Evaluation CY - Atlanta, GA, USA DA - 17.07.2016 KW - Aktive Thermografie KW - Thermische Diffusivität KW - Zerstörungsfreie Prüfung KW - Kohlenstofffaserverstärkter Kunststoff KW - CFK KW - Active thermography KW - Thermal diffusivity KW - Non-Destructive testing KW - Carbon fiber reinforced polymer KW - CFRP PY - 2017 SN - 978-0-7354-1474-7 DO - https://doi.org/10.1063/1.4974671 SN - 0094-243X VL - 1806 IS - 1 SP - UNSP 100006-1 EP - 11 PB - AIP Publishing CY - Melville, NY, USA AN - OPUS4-39332 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andres Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F.-S. A1 - Unger, Jörg F. T1 - Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins N2 - In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed. T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantificationin Computational Sciences and Engineering CY - Athen, Greece DA - 12.06.2023 KW - Model bias KW - Bayesian Uncertainty Quantification KW - Digital Twins KW - Gaussian Processes KW - Statistical Finite Element Method PY - 2023 UR - https://2023.uncecomp.org/ SP - 1 EP - 15 AN - OPUS4-58227 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -