Nondestructive Evaluation Techniques Research Articles

Study of factors affecting the magnetic sensing capability of shape memory alloys for non-destructive evaluation of cracks in concrete: using response surface methodology (RSM) and artificial neural network (ANN) approaches

Currently, the field of structural health monitoring (SHM) is focused on investigating non-destructive evaluation techniques for the identification of damages in concrete structures. Magnetic sensing has particularly gained attention among the innovative non-destructive evaluation techniques. Recently, the embedded magnetic shape memory alloy (MSMA) wire has been introduced for the evaluation of cracks in concrete components through magnetic sensing techniques while providing reinforcement as well. However, the available research in this regard is very scarce. This study has focused on the analyses of parameters affecting the magnetic sensing capability of embedded MSMA wire for crack detection in concrete beams. The response surface methodology (RSM) and artificial neural network (ANN) models have been used to analyse the magnetic sensing parameters for the first time. The models were trained using the experimental data obtained through literature. The models aimed to predict the alteration in magnetic flux created by a concrete beam that has a 1 mm wide embedded MSMA wire after experiencing a fracture or crack. The results showed that the change in magnetic flux was affected by the position of the wire and the position of the crack with respect to the position of the magnet in the concrete beam. RSM optimisation results showed that maximum change in magnetic flux was obtained when the wire was placed at a depth of 17.5 mm from the top surface of the concrete beam, and a crack was present at an axial distance of 8.50 mm from the permanent magnet. The change in magnetic flux was 9.50% considering the aforementioned parameters. However, the ANN prediction results showed that the optimal wire and crack position were 10 mm and 1.1 mm, respectively. The results suggested that a larger beam requires a larger diameter of MSMA wire or multiple sensors and magnets for crack detection in concrete beams.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review

Explainable artificial intelligence prediction of defect characterization in composite materials

Non-destructive evaluation (NDE) techniques are integral across diverse applications for void detection within composites. Infrared (IR) thermography (IRT) is a prevalent NDE technique that utilizes reverse heat transfer principles to infer defect characteristics by analyzing temperature distribution. Although the forward heat transfer problem is well-posed, its inverse counterpart lacks uniqueness, posing non-unique solutions. The present study performs simulations using finite element analysis (FEA) in defective (a penny-shaped defect) composites through which the heat transfer flux is modeled. A total of 2100 simulations with various defect positions and sizes (depth, size, and thickness) are executed, and the corresponding surface temperature vs. time and vs. distance diagrams are extracted. The FEA outputs provide ample input data for developing an explainable artificial intelligence (XAI) model to estimate the defect characteristics. A detailed feature engineering task is performed to select the representative information from the diagrams. Explainable decision tree-based machine learning (ML) models with transparent decision paths based on derived features are developed to predict the defect depth, size, and thickness. The ML models’ results suggest superb accuracy (R2 = 0.92 to 0.99) across all three defect characteristics. The provided workflow sets a benchmark applicable to a range of fields, including health monitoring.

FTnet: An integrated network for fusing multi-modal NDE data of lightning damage in aircraft composite materials

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage.

Measurement and Simulation of the Propagation of Impulsive Acoustic Emission Sources in Pipes

Abstract: Acoustic Emission (AE) testing is a non-destructive evaluation technique that has gained significant attention in pipeline monitoring. Pencil-lead breaks (PLBs) are commonly used in reproducing and characterising sensors used in AE applications and have emerged as a valuable tool for calibration processes. This technique involves breaking a pencil lead by pressing it on the surface of the test structure and applying a bending moment at a given angle on a surface. The applied force produces a local deformation on the test surface, which is released when the lead breaks. The fracture in these PLBs is assumed to be a step unload; however, this is not the case. In this work, a series of PLB source experiments complemented with parallel numerical simulations were carried out to investigate the actual unload rate by correlating the relationship between AE speed, frequency, and power from PLBs. This was achieved by varying the simulation unload rates recorded over a duration of 2 s on a steel pipe and comparing to the experiment. Analysis of the investigated results from the experimental and numerical models suggests that although the AE line structure of a PLB can be reproduced by simulation for short times only (1 µs), the actual unload rate for PLBs is in the region of 10–8 s. It is concluded that FEA has the potential to help in the recovery of the temporal structure from real AE structures. The establishment of this model will provide a theoretical basis for future studies on the monitoring of non-impulsive AE sources such as impact on pipelines using finite element analysis.

Utilizing Electrochemical Techniques for Assessing the Probability of Concrete Resistivity and Corrosion Potential in Reinforced Concrete Structures

Corrosion of reinforcing steel is a major durability issue for reinforced concrete structures exposed to aggressive environments. Early assessment of corrosion probability is important for effective maintenance planning. This study investigates the application of non-destructive electrochemical methods like concrete resistivity and half-cell potential measurement, along with integrated assessment and advanced techniques, for evaluating corrosion risk in reinforced concrete. Concrete specimens with and without Albizia amara resin coating were subjected to accelerated corrosion testing. Concrete resistivity and half-cell potential values showed an inverse correlation, with lower resistivity indicating higher corrosion risk. Resistivity and potential results for the coated samples exhibited intermediate values compared to the control and corroded samples, demonstrating the resin's partial passivating effects. Rebar diameter measurements before and after exposure confirmed corrosion led to reductions in diameter and cross-sectional area, which coating mitigated. Statistical analysis validated the significant impacts of original bar diameter and corrosion level on subsequent geometry changes. Mechanical testing of reinforcing steel bars found the control samples had the highest strength and ductility, while the coated bars exhibited properties closer to the controls than the corroded bars. The protective capabilities of the bio-based Albizia amara resin coating were validated through multipoint monitoring of material durability parameters. Overall, results demonstrated the effectiveness of integrated electrochemical testing with non-destructive techniques for comprehensive condition assessment and corrosion risk evaluation in reinforced concrete structures

Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves

This article proposes a deep learning-assisted nondestructive evaluation (NDE) technique for locating and sizing a coating delamination using ultrasonic guided waves. The proposed technique consists of sending a propagating guided wave into a coated plate with a transducer and measuring the corresponding time-domain signals by receivers at several locations at downstream distances from the source transducer. The received time-domain signals are then provided to a trained machine-learning (ML) algorithm, which subsequently outputs the location and size of any delamination flaws between the transducer and receivers. Numerical simulations show that the proposed NDE technique yields accurate results with high throughput, once the ML algorithm is well trained. Although training the ML algorithm is time-consuming, this training only needs to be done once for a given sample configuration. The results of this article demonstrate that the proposed technique has great potential for characterizing delamination flaws in practical NDE and structural health monitoring (SHM) applications.

Barely visible impact damage detection in CFRP composite using vibrothermography with narrowband sweep excitation

The extensive use of carbon fiber-reinforced plastic (CFRP) composite materials in various industries has posed new challenges for conducting effective structural health monitoring and nondestructive evaluation techniques. Based on local defect resonance (LDR), the vibrothermography with narrowband sweep excitation is proposed to detect BVIDs in the CFRP composite. The response spectrum of displacement at the impact crater was collected by a laser Doppler vibrometer, which was used to determine the frequency range of narrowband sweep excitation. The frequency range was further validated by the analysis of local defect resonance for thermal responses, and the sweep duration was also optimized. Subsequently, the proposed vibrothermography was employed to detect BVIDs with varying severity. The impact damage could be efficiently activated and a significant temperature rise was generated in the defective areas, which enhances the temperature contrast of the thermal images and facilitates the quantification of defects.

Acoustic Emission in Ceramic Matrix Composites

Abstract The integration of ceramic matrix composites (CMCs) into safety-critical applications, such as turbine engines and aerospace structures, necessitates a sound understanding of their expected damage evolution under in-service conditions and real-time health-monitoring methods to assess their damage state. The measurement of acoustic emissions (AEs), the transient elastic waves emitted during damage formation, offers an enhanced capability for evaluating damage evolution and structural health in CMCs due to its high sensitivity, accurate temporal resolution, and relative ease of use compared to other nondestructive evaluation (NDE) techniques. Recent advances in numerical simulation methods and data-driven model development, in combination with improved multimodal experimental characterization methods and sensor hardware, are rapidly advancing AE to a mature technique for damage quantification. This review discusses the fundamental principles of acoustic emissions, provides practical guidelines on their experimental characterization and analysis, and offers perspectives on the current state-of-the-art.

Ultrasonic assessment of aging in lithium metal pouch cells

Lithium metal anodes are prone to non-uniform lithium plating that can lead to dendrite formation, internal short circuits, and catastrophic ignition. This type of structural defect is not captured by battery management systems, prompting the need to advance nondestructive evaluation (NDE) techniques that are suitable for periodic monitoring of the internal structure. An ultrasonic inspection technique is evaluated here as a means of identifying flaws and irregular lithium plating that can be a precursor to dendrite formation and, ultimately, battery failure.Lithium metal pouch cell batteries were aged through electrochemical cycling at a range of moderate constant-current charging and discharging rates. They were inspected intermittently using a tailored local ultrasonic resonance spectroscopy (LURS) approach that measures the local through-thickness resonances of the battery as an indicator of the underlying structure. LURS results showed clear structural resonances in cells in the range of 3–7 MHz, which shifted significantly to lower frequencies as batteries aged. In addition to point measurements, scanning LURS measurements were implemented to map resonance frequencies over the electrode surface. These resonance maps revealed spatial variations in structure that developed during aging and showed better contrast to structural variation than achieved using conventional amplitude-based c-scans of the specimens.NDE results from pouch cell measurements were compared to a destructive physical analysis of the electrode structures using scanning electron microscopy (SEM). The SEM results showed differences in the end-of-life lithium anode structure at different charge rates. The results of this study show the capability of LURS as a means for identifying changes in battery structures during aging and establish resonance trends that may be applied in future work for prognostic modeling of batteries in service.

Consideration of electronic mean heat transport via a low dimension system

The object of research is the complex realm of energy localization and coherent ballistic electronic transport within low-dimensional silicon quantum wires, specifically those doped with germanium atoms. Unlike their three-dimensional counterparts, low-dimensional systems exhibit unique electronic transport behaviors, necessitating novel analytical approaches for a comprehensive understanding. The core of this investigation leverages the Phase Field Matching Theory (PMFT) and the tight-binding (TB) approximation, sophisticated methodologies that enable a deep dive into the quantum mechanical nuances of these systems. Through this lens, we examine the intricate dynamics of dispersion relationships, phase factors, group velocities, and notably, the impact of defects introduced by the germanium doping. This research meticulously analyzes how these defects affect electronic and thermal conductivities, along with densities of states, offering new insights into the role of Fano resonances in the fluctuation of transmission and reflection spectra. These resonances, we find, are crucially dependent on the nature of the defects, their configuration, and the electronic parameters in their vicinity, underscoring the nuanced interplay between material composition and electronic properties in low-dimensional systems. The implications of our findings extend far beyond the theoretical. They pave the way for significant advancements in nanotechnology and the design of electronic devices, highlighting the potential for creating more efficient, high-performance components. Furthermore, this work proposes a framework for developing non-destructive testing methodologies that could revolutionize material science by enabling the precise analysis of defects in low-dimensional systems without causing damage. This is particularly critical for the ongoing development of materials with optimized properties for various applications, from electronics to energy storage. In essence, this research not only enriches our understanding of the physics governing low-dimensional systems but also offers practical insights into leveraging these properties for technological innovation. By bridging the gap between theoretical physics and material science, our study sets the stage for the next generation of electronic components and non-destructive evaluation techniques, marking a significant step forward in the application of quantum mechanics to real-world challenges.

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R2) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

Performance evaluation of a magnetic NDE technique to assess residual fatigue life of the retired hydraulic cylinders

Metal magnetic memory (MMM) method is a novel and potential nondestructive evaluation (NDE) technique to assess residual fatigue life of the ferromagnetic structure, such as hydraulic cylinder. In this paper, an evaluation framework was proposed. Firstly, the MMM signals under different fatigue cycles were recorded in the tensile-tensile experiment. A representative signal characteristic Hpp was defined as an evaluation index. The monotonic change trend and abrupt change point was investigated. Statistical results show that Hpp can be used to predict residual fatigue life. Except for a few specimens, almost all specimens produced an abrupt change at the normalized fatigue life coefficient k = 0.91. This can be used to distinguish different stages of fatigue life. The sensitivity, specificity, and diagnostic performance were analyzed using Receiver Operating Characteristic (ROC) curves. The statistical results show that when the coefficient k = 0.91, the diagnostic performance is relatively best with an area under the curve (AUC) is about 0.85, and Jorden's index is about 0.75.

A priority-based self-guided serial–parallel genetic algorithm for low-dose computed tomography

Computed tomography (CT) is a non-destructive evaluation technique to know the internal structure of the objects under scan. It has numerous applications in engineering as well as in the medical field. The prime objective of this manuscript is to reduce the radiation dose to the object under scanning and to reconstruct the low-noise CT images, even in limited-view projections data scenarios. The present manuscript proposes a novel priority-based self-guided serial-parallel hybrid genetic algorithm for low-dose CT reconstruction. The current algorithm combines serial and parallel processing elements to reduce the loss of diversity in the population and increase the convergence rate. The proposed algorithm uses a novel priority-based self-guided competition operator. It consists of two approaches for maintaining exploration and exploitation tradeoffs. Here, the first approach uses serial processing, whereas the second approach uses parallel processing. The algorithm also uses priority-based hybrid crossover (PHCO) and adaptive priority-based mutation operator (PMO) to generate the most suitable offspring in every generation. Experimental results reveal that the presented algorithm produces satisfactory results with limited-view projections. The presented algorithm also outperforms other limited-view CT reconstruction algorithms.

Early detection of steel tube welded joint failure using SPC-I nonlinear ultrasonic technique

Welding is a commonly used method for joining two or more parts together in steel construction. Various defects in weld regions such as cracks, pores, and slag inclusion can be present from the beginning, generated during the welding process, or can be developed while in service. Such defects are the weak spots that degrade the structure’s quality and can lead to structural failures. Therefore, early detection of these defects in welded joints, before visible cracks appear, is very important. Using appropriate ultrasonic non-destructive testing and evaluation (NDT&E) techniques, one can detect these defects and take remedial actions to prevent catastrophic structural failures. Guided acoustic wave-based techniques have been proven to be effective for damage detection in steel pipes and rods. Several studies have previously attempted to detect damage in steel tubes using guided ultrasonic waves. Unlike earlier attempts which mostly focused on conventional linear ultrasonic techniques, a relatively new nonlinear ultrasonic technique called sideband peak count-index (SPC-I) is carried out in this research. For this investigation, cast steel components and round hollow structural sections are welded together, and a four-point bending test is conducted under fatigue loading. The welded joints are continuously monitored in real time using strain gages and lead zirconate titanate (PZT) transducers. The PZT transducers are used to generate and receive guided acoustic waves. The signal is propagated through the specimen in a single-sided transmission mode setup. The strain gage readings and the nonlinear ultrasonic parameter, the SPC-I values, are monitored simultaneously. The results obtained from the nonlinear ultrasonic NDT&E measurements are compared with the data obtained from the strain gages to determine the robustness and reliability of the SPC-I technique for monitoring welded joints. This investigation also shows the potential effectiveness of the nonlinear ultrasonic parameter SPC-I for early detection of weld failure.

Synthetic data generation using finite element method to pre-train an image segmentation model for defect detection using infrared thermography

AbstractThe vision of a deep learning-empowered non-destructive evaluation technique aligns perfectly with the goal of zero-defect manufacturing, enabling manufacturers to detect and repair defects actively. However, the dearth of data in manufacturing is one of the biggest obstacles to realizing an intelligent defect detection system. This work presents a framework for bridging the data gap in manufacturing using the potential of synthetic datasets generated using the finite element method-based digital twin. The non-destructive technique under consideration is pulse infrared thermography. A large number of synthetic thermographic measurements were generated using 2D axisymmetric transient thermal simulations. The representativeness of synthetic data was thoroughly investigated at various steps of the framework, and the image segmentation model was trained separately on experimental and synthetic datasets. The study results reveal that when carefully rendered, synthetic datasets represent the experimental data well. When evaluated on real-world experimental samples, the segmentation model pre-trained on synthetic datasets generalizes well to the experimental samples. Furthermore, another advantage of synthetic datasets is the ease of labelling a large amount of data. Finally, the robustness assessment of the model was done on two new datasets: one where the complete experimental setup was changed, and the other was an open-source infrared thermography dataset

An Evaluation of ASTM Standards for Implementation of Ground Penetrating Radar for Pavement and Bridge Deck Evaluations

Abstract Ground penetrating radar (GPR) is a common nondestructive evaluation technique that operates with electromagnetic energy (EM) to detect objects and anomalies in a material’s subsurface. GPR is an effective method for civil engineering applications for the assessment of structures and roadways specifically for pavement and bridge deck condition assessment. While surveying with GPR is straightforward, requirements differ depending on the testing objective, and interpretation of the results can pose some challenges. Fortunately, the American Society for Testing Materials (ASTM) provides a standard set of suggestions for the utilization of GPR, which include general information and recommendations for operating GPR systems and analyzing the output for different purposes. However, reaching a full comprehensive understanding of each standard may require some additional knowledge. This paper focuses on three separate ASTM standards that are related to GPR implementations. These standards include the following: ASTM D6432-19, Standard Guide for Using the Surface Ground Penetrating Radar Method for Subsurface Investigation; ASTM D4748-10(2020), Standard Test Method for Determining the Thickness of Bound Pavement Layers Using Short-Pulse Radar; and ASTM D6087-22, Standard Test Method for Evaluating Asphalt-Covered Concrete Bridge Decks Using Ground Penetrating Radar. After a review and assessment of these three standards, an analysis is presented to discuss the applications, contemplations, and limitations of the standards.

An electrochemical impedance spectroscopy study on ion-fouling of forward osmosis membranes

Forward osmosis (FO) has been rapidly developed and employed in the industrial and environmental wastewater treatment, whose performance is affected profoundly by membrane fouling. Electrochemical impedance spectroscopy (EIS), as a nondestructive and low-cost evaluation technique, has been increasingly used for the characterization of membrane structures and membrane fouling. However, monitoring ion fouling of FO membranes is difficult with EIS due to the weak signal. In this work, the ion-fouling of various FO membranes was successfully characterized by EIS and the inductance variation was firstly utilized to interpret the degree of FO membrane fouling. Multi-walled carbon nanotubes (MWCNTs) and polydopamine (PDA) were modified onto the thin film nanofibrous composite membrane (#FO-0) as anti-fouling modification (#FO-MWCNTs and #FO-PDA), respectively. The structural measurements of #FO-0, #FO-MWCNTs, and #FO-PDA were achieved by a two-electrode EIS system. Then the ion fouling of FO membranes was monitored by a four-electrode EIS system. Through the equivalent circuit fitting, the correlation between the inductance variation and the membrane fouling was established. The FO experiments further validated the effectiveness of the proposed EIS method. The results indicate that inductance as a detection index is expected to be a convenient tool for studying the ion-fouling phenomenon in the FO process.

Enhanced Global Convolution Networks for Defect Detection in Full Wavefield Imaging

Non-Destructive Evaluation (NDE) techniques based on ultrasonic guided waves can provide relevant information about the health status of an inspected structure. The analysis of the wavefields recorded through Scanning Laser Doppler Vibrometers (SLDVs) has been used successfully in NDE systems to detect damages in planar isotropic materials, such as interlaminar fractures in composites. However, the high spatial point resolution required to ensure an accurate localization and quantification of defects typically implies a time-consuming acquisition process, which limits the applicability of such approaches. In this work, we exploit the potential of convolutional neural networks (CNNs) to perform segmentation on full wavefield images, obtained by reconstruction from a low resolution grid having a spatial sampling rate below the Nyquist frequency, for the purpose of detecting and localising delaminations in carbon fibre reinforced plastic (CFRP) plates. In particular, we trained an improved version of Global Convolutional Networks (GCN) using a public dataset containing 475 simulated cases of full wavefield Lamb waves propagation in a CFRP plate, generated by an actuator with a carrier frequency of 50 kHz. We adopted channel and spatial attention mechanisms to improve the accuracy of the networks and applied our method on (i) the resulting image given by the root mean square value (RMS) in time for each spatial position and on (ii) the 3-dimensional animation representing the full wavefield propagation. Our networks show the ability to accurately locate target damages with a spatial resolution 8 times higher than the dimension of the adopted sampling grid, achieving an Intersection over Union score equal to 0.78 with a number of scanning point more than 60 times lower than the number of pixels in the output segmentation mask.

Utilizing the derivative of unwrapped phase in ultrasonic nondestructive evaluation of elastic properties of polymer filaments.

Polymer filaments represent the fundamental materials employed in fused filament fabrication additive manufacturing. This paper uses an innovative nondestructive evaluation technique for gauging the elastic properties of these polymer filaments. The method hinges on acoustic wave scattering theory, wherein a polymer filament is immersed in water and exposed to an incident acoustic wave. The waves scattered from the cylinder contain crucial information concerning the elastic properties of the filament. To extract these properties, an inverse method is applied which compares the resonance frequencies of the scattered signal with those anticipated based on a theoretical model. To improve the performance of this method in identifying the resonance frequencies, the derivative of the unwrapped phase within the backscattered pressure spectrum is analyzed. This is an advantageous approach as it can be equally applied to both experimental and theoretical pressure spectra, simplifying the task of identification of resonance frequencies considerably. The method's effectiveness is exemplified through its application to an aluminum rod followed by its application to two polymer filaments. Comparing the elastic constants of the polymer filaments determined through the proposed method with values reported in the literature underscores the method's capability to accurately measure the elastic constants of polymer filaments.