Industry 4.0 is based on digitization, information crosslinking and networks. In this investigation an Industrial Internet of Things (IIoT) architecture developed by the authors is used in conjunction with NDE datasets for near real-time diagnostics of crack initiation. Acoustic Emission datasets were acquired using aerospace-grade aluminum alloy and were subsequently used in the IIoT system, which is capable of Edge, Fog, and Cloud computing. The main innovation of this approach is a combination of hardware, computing and Machine Learning analysis proves to be advantageous in implementing a data structure that can successfully flag the incubation and subsequent initiation of fracture.
Richard Vallett, Chelsea Knittel, Daniel Christe, Nestor Castaneda, Christina Kara, Krzysztof Mazur, Dani Liu, Antonios Kontsos, Youngmoo Kim, Genevieve Dion
Digital fabrication methods are reshaping design and manufacturing processes through the adoption of pre-production visualization and analysis tools, which help minimize waste of materials and time. Despite the increasingly widespread use of digital fabrication techniques, comparatively few of these advances have benefited the design and fabrication of textiles. The development of functional fabrics such as knitted touch sensors, antennas, capacitors, and other electronic textiles could benefit from the same advances in electrical network modeling that revolutionized the design of integrated circuits. In this paper, the efficacy of using current state-of-the-art digital fabrication tools over the more common trialand- error methods currently used in textile design is demonstrated. Gaps are then identified in the current state-of-the-art tools that must be resolved to further develop and streamline the rapidly growing field of smart textiles and devices, bringing textile production into the realm of 21st century manufacturing.
This study focuses on deformability and damage detection of a concrete masonry wall. It employed point-to-point traditional strain gages and full-field measurement technique using digital image correlation (DIC) to investigate the damage and deformability of a partially grouted (PG) reinforced masonry wall. A set of ungrouted and grouted assemblages and full-scale concrete masonry shear wall were constructed and tested under displacement control loading. The wall was constructed according with masonry standards joint committee (MSJC 2013) and tested under constant vertical compression load and horizontal lateral load using quasi-static displacement procedure. The DIC method was used to determine non-uniform strain contours on the assemblages. This method was verified by comparing strains along the selected directions with traditional TML gage results. After a successful comparison, the method was used to investigate the state of damage and deformability of the wall specimen. Panel deformation, crack pattern, displacement at the top, and the base strain of the wall were captured using full-field measurement and results were in a good agreement with traditional strain gages. It is concluded that full-filed measurements using DIC is promising especially when the test specimens experience inelastic deformation and high degree of damage. The ability to characterize and anticipate failure mechanisms of concrete masonry systems by depicting strain distribution, categorizing structural cracks and investigating their effects on the behavior of the wall were also shown using DIC. In addition to monitoring strains across the gage length, the DIC method provided full-field strain behavior of the test specimens and revealed strain hotspots at locations that corresponded to failure.
Many envision that in the near future the application of Unmanned Aerial Vehicles (UAVs) will impact the civil engineering industry. Use of UAVs is currently experiencing tremendous growth, primarily in military and homeland security applications. It is only a matter of time until UAVs will be widely accepted as platforms for implementing monitoring/surveillance and inspection in other fields. Most UAVs already have payloads as well as hardware/software capabilities to incorporate a number of non-contact remote sensors, such as high resolution cameras, multi-spectral imaging systems, and laser ranging systems (LIDARs). Of critical importance to realizing the potential of UAVs within the infrastructure realm is to establish how (and the extent to which) such information may be used to inform preservation and renewal decisions. Achieving this will depend both on our ability to quantify information from images (through, for example, optical metrology techniques) and to fuse data from the array of non-contact sensing systems. Through a series of applications to both laboratory-scale and field implementations on operating infrastructure, this paper will present and evaluate (through comparison with conventional approaches) various image processing and data fusion strategies tailored specifically for the assessment of highway bridges. Example scenarios that guided this study include the assessment of delaminations within reinforced concrete bridge decks, the quantification of the deterioration of steel coatings, assessment of the functionality of movement mechanisms, and the estimation of live load responses (inclusive of both strain and displacement).
This paper represents a hybrid non-destructive testing (HNDT) approach based on infrared thermography (IRT), acoustic emission (AE) and ultrasonic (UT) techniques for effective damage quantification of partially grouted concrete masonry walls (CMW). This integrated approach has the potential to be implemented for the health monitoring of concrete masonry systems. The implementation of this hybrid approach assists the cross validation of in situ recorded information for structural damage assessment. In this context, NDT was performed on a set of partially grouted CMW subjected to cyclic loading. Acoustic emission (AE) signals and Infrared thermography (IRT) images were recorded during each cycle of loading while the ultrasonic (UT) tests were performed in between each loading cycle. Four accelerometers, bonded at the toe of the wall, were used for recording waveforms for both passive (AE) and active (UT) acoustics. For the active approach, high frequency stress waves were generated by an instrumented hammer and the corresponding waveforms were recorded by the accelerometers. The obtained AE, IRT, and UT results were correlated to visually confirm accumulated progressive damage throughout the loading history. Detailed post-processing of these results was performed to characterize the defects at the region of interest. The obtained experimental results demonstrated the potential of the methods to detect flaws on monitored specimens; further experimental investigations are planned towards the quantitative use of these NDT methods.
A cross-validated nondestructive evaluation approach was employed to in situ detect the onset of damage in an
Aluminum alloy compact tension specimen. The approach consisted of the coordinated use primarily the acoustic
emission, combined with the infrared thermography and digital image correlation methods. Both tensile loads were
applied and the specimen was continuously monitored using the nondestructive approach. Crack initiation was
witnessed visually and was confirmed by the characteristic load drop accompanying the ductile fracture process. The
full field deformation map provided by the nondestructive approach validated the formation of a pronounced
plasticity zone near the crack tip. At the time of crack initiation, a burst in the temperature field ahead of the crack
tip as well as a sudden increase of the acoustic recordings were observed. Although such experiments have been
attempted and reported before in the literature, the presented approach provides for the first time a cross-validated
nondestructive dataset that can be used for quantitative analyses of the crack initiation information content. It further
allows future development of automated procedures for real-time identification of damage precursors including the
rarely explored crack incubation stage in fatigue conditions.
This research demonstrates the use of Digital Image Correlation (DIC) as a non-contact, non-destructive testing and evaluation (NDT and E) technique by presenting experimental results pertinent to damage monitoring and quantification in several material systems at different length scales of interest. At the microstructural level compact tension aluminum alloy specimens were tested under Mode I loading conditions using an appropriate field of view to track grain scale crack initiation and growth. The results permitted the quantification of the strain accumulation near the tip of the fatigue pre-crack, as well as the computation of the relevant crack opening displacement as a function of crack length. At the mesoscale level, damage quantification in fiber reinforced composites subject to both tensile and fatigue loading conditions was achieved by using the DIC as part of a novel integrated NDT approach combining both acoustic and thermal methods. DIC in these experiments provided spatially resolved and high accuracy strain measurements capable to track the formation of damage "hot spots" that corresponded to the sites of the ultimately visible fracture pattern, while it further allowed the correlation of mechanical parameters to thermal and acoustic features. Finally, at the macrostructural level DIC measurements were also performed and compared to traditional displacement gauges mounted on a steel deck model subject to both static and dynamic loads, as well as on masonry structures including hollow and grouted concrete walls.
Reliable damage detection and quantification is a difficult process because of its dynamic and
multi-scale nature, which combined with material complexities and countless other sources of
uncertainty often inhibits a single non-destructive testing (NDT) technique to successfully
evaluate the extension of deterioration in critical structural components. This paper presents an
integrated non-destructive testing approach (INDT) for effective damage identification relying
on the intelligent integration of the Acoustic Emission (AE), Guided Ultrasonic Waves (GUW)
and Digital Image Correlation (DIC) methods. The proposed system has been utilized to identify
wire breaks in seven-wire steel strands and crack initiation and development in masonry concrete
walls and is based on the cross-correlation of heterogeneous damage-related NDT features.
Conventional AE monitoring relies on damage monitoring by evaluating multiple extracted
and/or computed features as a function of load/time. In addition, advanced post-processing
methods including mathematical algorithms for statistical analysis and classification have been
suggested to improve the robustness of AE in damage identification. Unfortunately, such
approaches are often found to be unsuccessful, due to challenging environmental and operational
conditions, as well as when used on actual civil structural components, such as bridge cables and
masonry walls. This paper presents the framework for successful correlation of AE features with
GUW and mechanical parameters such as full field strain maps, which can provide a route
towards actual cross-validated damage assessment, capable to detect the initiation and track the
development of damage in structures. The presented INDT approach could lead to reliable
damage identification approaches in mechanical, aerospace and civil infrastructure applications.
A data fusion technique implementing the principles of acoustic emission (AE), ultrasonic testing (UT) and digital
image correlation (DIC) was employed to in situ monitor crack propagation in an Al 2024 alloy compact tension
(CT) specimen. The specimen was designed according to ASTM E647-08 and was pre-cracked under fatigue
loading to ensure stable crack growth. Tensile (Mode I) loads were applied according to ASTM E1290-08 while
simultaneously recording AE activity, transmitting ultrasonic pulses and measuring full-field surface strains. Realtime
2D source location AE algorithms and visualization provided by the DIC system allowed the full quantification
of the crack growth and the cross-validation of the recorded non-destructive testing data. In post mortem, waveform
features sensitive to crack propagation were extracted and visible trends as a function of computed crack length were
observed. In addition, following a data fusion approach, features from the three independent monitoring systems
were combined to define damage sensitive correlations. Furthermore a novelty detector based on the Mahalanobis
outlier analysis was implemented to quantify the extent of crack growth and to define a more robust sensing basis
for the proposed system.
A continuum thermodynamics framework is presented to model the evolution of domain structures in active/smart
materials. To investigate the consequences of the theories, fundamental defect interactions are studied. A principle of
virtual work is specified for the theory and is implemented to devise a finite element formulation. For ferroelectrics, the
theory and numerical methods are used to investigate the interactions of 180° and 90° domain walls with arrays of
charged defects and dislocations to determine how strongly domain walls are electromechanically pinned by the arrays of
defects. Additionally, the problems of nucleation and growth of domains from crack tips, and the propagation of domain
needles are studied. The importance of adaptive mesh refinement and coarsening is discussed in the context of this
modeling approach.
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