scholarly journals Uncertainty quantification for impact location and force estimation in composite structures

2021 ◽  
pp. 147592172110202
Author(s):  
Aldyandra Hami Seno ◽  
MH Ferri Aliabadi
Keyword(s):  
Uncertainty Quantification ◽  
Lamb Wave ◽  
Measurement Errors ◽  
Composite Structures ◽  
Data Driven ◽  
Maximum Force ◽  
Reliable Estimate ◽  
Reference Database ◽  
Force Estimation ◽  
Impact Location

Structural health monitoring of impact location and severity using Lamb waves has been proven to be a reliable method under laboratory conditions. However, real-life operational and environmental conditions (vibration noise, temperature changes, different impact scenarios, etc.) and measurement errors are known to generate variation in Lamb wave features which may significantly affect the accuracy of these estimates. Therefore, these uncertainties should be considered, as a deterministic approach may lead to erroneous decisions. In this article, a novel data-driven stochastic Kriging-based method for impact location and maximum force estimation, that is able to reliably quantify the output uncertainty is presented. The method utilises a novel modification of the kriging technique (normally used for spatial interpolation of geostatistical data) for statistical pattern matching and uncertainty quantification using Lamb wave features to estimate the location and maximum force of impacts. The data was experimentally obtained from a composite panel equipped with piezoelectric sensors. Comparison with a deterministic benchmark method developed in prior studies shows that the proposed method gives a more reliable estimate for experimental impacts under various simulated environmental and operational conditions by estimating the uncertainty. The developed method highlights the suitability of data-driven methods for uncertainty quantification, by taking advantage of the relationship between data points in the reference database that is a mandatory component of these methods (and is often seen as a disadvantage). By quantifying the uncertainty, there is more information for operators to reliably locate impacts and estimate the severity, leading to robust maintenance decisions.

scholarly journals Adaptability of Ultrasonic Lamb Wave Touchscreen to the Variations in Touch Force and Touch Area

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1736
Author(s):  
Zengchong Yang ◽  
Xiucheng Liu ◽  
Bin Wu ◽  
Ren Liu
Keyword(s):  
Lamb Wave ◽  
Weight Coefficient ◽  
The Self ◽  
Reference Database ◽  
Learning Method ◽  
Improved Method ◽  
Large Area ◽  
Localization Model ◽  
Reference Databases ◽  
First Time

Previous studies on Lamb wave touchscreen (LWT) were carried out based on the assumption that the unknown touch had the consistent parameters with acoustic fingerprints in the reference database. The adaptability of LWT to the variations in touch force and touch area was investigated in this study for the first time. The automatic collection of the databases of acoustic fingerprints was realized with an experimental prototype of LWT employing three pairs of transmitter–receivers. The self-adaptive updated weight coefficient of the used transmitter–receiver pairs was employed to successfully improve the accuracy of the localization model established based on a learning method. The performance of the improved method in locating single- and two-touch actions with the reference database of different parameters was carefully evaluated. The robustness of the LWT to the variation of the touch force varied with the touch area. Moreover, it was feasible to locate touch actions of large area with reference databases of small touch areas as long as the unknown touch and the reference databases met the condition of equivalent averaged stress.

Selection of Materials and Sensors for Health Monitoring of Composite Structures

2003 ◽  
Vol 785 ◽  
Author(s):  
Seth S. Kessler ◽  
S. Mark Spearing
Keyword(s):  
Health Monitoring ◽  
Lamb Wave ◽  
Composite Structures ◽  
Structural Damage ◽  
Performance Metrics ◽  
Full Range ◽  
Transportation Systems ◽  
Health Monitoring Systems ◽  
The Relationship ◽  
Selection Of

ABSTRACTEmbedded structural health monitoring systems are envisioned to be an important component of future transportation systems. One of the key challenges in designing an SHM system is the choice of sensors, and a sensor layout, which can detect unambiguously relevant structural damage. This paper focuses on the relationship between sensors, the materials of which they are made, and their ability to detect structural damage. Sensor selection maps have been produced which plot the capabilities of the full range of available sensor types vs. the key performance metrics (power consumption, resolution, range, sensor size, coverage). This exercise resulted in the identification of piezoceramic Lamb wave transducers as the sensor of choice. Experimental results are presented for the detailed selection of piezoceramic materials to be used as Lamb wave transducers.

scholarly journals Wave Propagation Analysis in Composite Laminates Containing a Delamination Using a Three-Dimensional Spectral Element Method

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Fucai Li ◽  
Haikuo Peng ◽  
Xuewei Sun ◽  
Jinfu Wang ◽  
Guang Meng
Keyword(s):  
Wave Propagation ◽  
Lamb Wave ◽  
Composite Laminates ◽  
Composite Structures ◽  
Spectral Element Method ◽  
Three Dimensional ◽  
Spectral Element ◽  
Wave Mode ◽  
Diagonal Form ◽  
Element Method

A three-dimensional spectral element method (SEM) was developed for analysis of Lamb wave propagation in composite laminates containing a delamination. SEM is more efficient in simulating wave propagation in structures than conventional finite element method (FEM) because of its unique diagonal form of the mass matrix. Three types of composite laminates, namely, unidirectional-ply laminates, cross-ply laminates, and angle-ply laminates are modeled using three-dimensional spectral finite elements. Wave propagation characteristics in intact composite laminates are investigated, and the effectiveness of the method is validated by comparison of the simulation results with analytical solutions based on transfer matrix method. Different Lamb wave mode interactions with delamination are evaluated, and it is demonstrated that symmetric Lamb wave mode may be insensitive to delamination at certain interfaces of laminates while the antisymmetric mode is more suited for identification of delamination in composite structures.

Damage detection in composite structures using Lamb wave analysis and time-frequency approach

2011 ◽  
Author(s):  
Yingtao Liu ◽  
Masoud Yekani Fard ◽  
Seung B. Kim ◽  
Aditi Chattopadhyay ◽  
Derek Doyle
Keyword(s):  
Damage Detection ◽  
Lamb Wave ◽  
Composite Structures ◽  
Wave Analysis ◽  
Time Frequency

Coherent, data-driven Lamb wave localization under environmental variations

2015 ◽  
Author(s):  
Joel B. Harley ◽  
Chang Liu ◽  
Irving J. Oppenheim ◽  
David W. Greve ◽  
José M.F. Moura
Keyword(s):  
Lamb Wave ◽  
Data Driven ◽  
Wave Localization ◽  
Environmental Variations

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Mark F. Horstemeyer ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Temperature Field ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Data Driven ◽  
Modeling Framework ◽  
High Quality ◽  
Experimental Calibration ◽  
Multi Scale ◽  
Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

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