scholarly journals Detection of Micro-Cracks in Metals Using Modulation of PZT-Induced Lamb Waves

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3823
Author(s):  
Sang Eon Lee ◽  
Jung-Wuk Hong
Keyword(s):  
Quantitative Analysis ◽  
Crack Length ◽  
Crack Detection ◽  
Lamb Waves ◽  
Early Stage ◽  
Fatigue Cracks ◽  
Excitation Frequency ◽  
High Sensitivity ◽  
Detection Algorithm ◽  
Modulated Waves

The ultrasonic modulation technique, developed by inspecting the nonlinearity from the interactions of crack surfaces, has been considered very effective in detecting fatigue cracks in the early stage of the crack development due to its high sensitivity. The wave modulation is the frequency shift of a wave passing through a crack and does not occur in intact specimens. Various parameters affect the modulation of the wave, but quantitative analysis for each variable has not been comprehensively conducted due to the complicated interaction of irregular crack surfaces. In this study, specimens with a constant crack width are manufactured, and the effects of various excitation parameters on modulated wave generation are analyzed. Based on the analysis, an effective crack detection algorithm is proposed and verified by applying the algorithm to fatigue cracks. For the quantitative analysis, tests are repeatedly conducted by varying parameters. As a result, the excitation intensity shows a strong linear relationship with the amount of modulated waves, and the increase of modulated wave is expected as crack length increases. However, the change in the dynamic characteristics of the specimen with the crack length is more dominant in the results. The excitation frequency is the most dominant variable to generate the modulated waves, but a direct correlation is not observed as it is difficult to measure the interaction of crack surfaces. A numerical analysis technique is developed to accurately simulate the movement and interaction of the crack surface. The crack detection algorithm, improved by using the observations from the quantitative analyses, can distinguish the occurrence of modulated waves from the ambient noises, and the state of the specimens is determined by using two nonlinear indexes.

Effect of Crack Closure on Magnitude of Modulated Wave

2020 ◽  
Vol 20 (13) ◽  
pp. 2041018
Author(s):  
Sang Eon Lee ◽  
Jung-Wuk Hong
Keyword(s):  
Crack Detection ◽  
Fatigue Cracks ◽  
Structural Components ◽  
Nonlinear Ultrasonic ◽  
Crack Tips ◽  
Buckling Failure ◽  
Modulated Waves ◽  
Structural Failures ◽  
External Forces ◽  
Cracked Specimens

Fatigue cracks generated by repeated loads cause structural failures. Such cracks grow continuously and at an increasing speed owing to the concentration of stresses near the crack tips. Therefore, the early detection of fatigue cracks is imperative in the field of structural-health monitoring for the safety of structures exposed to dynamic loading. In particular, the detection of those cracks subjected to compression is known as a challenging problem in the nondestructive inspection area. The nonlinear ultrasonic modulation technique is effective for the detection of microcracks smaller than the size of a wavelength because this technique uses the deformation of waves passing through the crack surfaces. However, the technique has not been thoroughly verified for detecting cracks subjected to external forces. In this study, nonlinear ultrasonic modulation tests are performed on two types of crack specimens under compressive forces. The results show that in fatigue-cracked specimens, the cracks can be detected using modulated waves even under strong compressions. With artificial cracks, buckling occurs at a relatively low compression, and the amounts of modulated waves rapidly increase due to the bending of the specimen before buckling failure takes place. In this study, the crack detection methodology under compression is proposed and experimentally verified. The proposed method might be beneficial to find cracks under compression in various structural components.

Development of a “stick-and-detect” wireless sensor node for fatigue crack detection

2016 ◽  
Vol 16 (2) ◽  
pp. 153-163 ◽  
Author(s):  
Peipei Liu ◽  
Hyung Jin Lim ◽  
Suyoung Yang ◽  
Hoon Sohn ◽  
Cheul Hee Lee ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Sensor Node ◽  
Crack Detection ◽  
Early Stage ◽  
Fatigue Cracks ◽  
Ultrasonic Waves ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Target Structure ◽  
Fatigue Crack Detection

A fatigue crack and its precursor often serves as a source of nonlinear mechanism for ultrasonic waves, and nonlinear ultrasonic techniques have been widely studied to detect fatigue crack at its very early stage. In this study, a wireless sensor node based on nonlinear ultrasonics is developed specifically for fatigue crack detection: (1) through packaged piezoelectric transducers, ultrasonic waves at two distinctive frequencies are generated, and their modulation due to a microcrack (less than 0.1 mm in width) is detected; (2) an autonomous reference-free crack detection algorithm is developed and embedded into the sensor node, so that users can simply “stick” the sensor to a target structure and automatically “detect” a fatigue crack without relying on any history data of the target structure; and (3) the whole design of the sensor node is fulfilled in a low-power working strategy. The performance of the sensor node is experimentally validated using aluminum plates with real fatigue cracks and compared with that of a conventional wired system. Furthermore, a field test in Yeongjong Grand Bridge in South Korea has been conducted with the developed sensor nodes.

scholarly journals Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach

2021 ◽  
Vol 3 (4) ◽  
pp. 353-364
Author(s):  
R. Kanthavel
Keyword(s):  
Early Detection ◽  
Crack Detection ◽  
Early Stage ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Research Article ◽  
Effective Manner ◽  
Main Fracture ◽  
Fracture Identification

Recently, glass crack detection methods have been emerging in Artificial intelligence programming. The early detection of the crack in glass could save many lives. Glass fractures can be detected automatically using machine vision. However, this has not been extensively researched. As a result, a detection algorithm is a benefit to study the mechanics of glass cracking. To test the algorithm, benchmark data are used and analysed. According to the first findings, the algorithm is capable of figuring out the screen more or less correctly and identifying the main fracture structures with sufficient efficiency required for majority of the applications. This research article has addressed the early detection of glass cracks by using edge detection, which delivers excellent accuracy in fracture identification. Following the pre-processing stage, the CNN technique extracts additional characteristics from the input pictures that have been provided due to dense feature extraction. The "Adam" optimizer is used to update the bias weights of networks in a cost-effective manner. Early identification is achievable with high accuracy metrics when using these approaches, as shown in the findings and discussion part of this paper.

Fatigue Characteristics of Vulcanized Natural Rubber for Automotive Engine Mounting

2007 ◽  
Vol 353-358 ◽  
pp. 178-181 ◽  
Author(s):  
S. Shibata ◽  
K. Ochi ◽  
Y. Aono ◽  
Hiroshi Noguchi ◽  
Hideki Oshima
Keyword(s):  
Fatigue Crack ◽  
Natural Rubber ◽  
Crack Length ◽  
Early Stage ◽  
Fatigue Cracks ◽  
Fatigue Tests ◽  
Miner’S Rule ◽  
Fatigue Characteristics ◽  
Stress Ratios ◽  
Miner's Rule

In order to investigate fatigue characteristics of vulcanized natural rubber (NR), fatigue tests are carried out under various stress ratios R (R = minimum stress / maximum stress). It was considered that the fatigue cracks were initiated from flaws in very early stage of total life. The fatigue damage process was almost the fatigue crack propagation process and it is independent of R. The crack growth rate was proportional to the crack length to about the first power, when the crack length was defined as the length of the direction perpendicular to the loading direction. Miner’s rule was examined to observe the fatigue crack behavior and checked by using two-step loading fatigue tests experimentally. It seems Miner’s rule has a possibility to predict fatigue lives.

Fatigue crack detection method using analysis of vibration signal

2017 ◽  
Vol 1 (20) ◽  
pp. 63-74 ◽  
Author(s):  
Arkadiusz Rychlik ◽  
Krzysztof Ligier
Keyword(s):  
Crack Detection ◽  
Fatigue Cracks ◽  
Fatigue Cracking ◽  
Vibration Signal ◽  
Technical Condition ◽  
Visual Methods ◽  
Signal Characteristics ◽  
Fatigue Crack Detection ◽  
Sample Structure ◽  
Change Identification

This paper discusses the method used to identify the process involving fatigue cracking of samples on the basis of selected vibration signal characteristics. Acceleration of vibrations has been chosen as a diagnostic signal in the analysis of sample cross section. Signal characteristics in form of change in vibration amplitudes and corresponding changes in FFT spectrum have been indicated for the acceleration. The tests were performed on a designed setup, where destruction process was caused by the force of inertia of the sample. Based on the conducted tests, it was found that the demonstrated sample structure change identification method may be applied to identify the technical condition of the structure in the aspect of loss of its continuity and its properties (e.g.: mechanical and fatigue cracks). The vibration analysis results have been verified by penetration and visual methods, using a scanning electron microscope.

scholarly journals Identification of BHLHE40 expression in peripheral blood mononuclear cells as a novel biomarker for diagnosis and prognosis of hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pattapon Kunadirek ◽  
Chaiyaboot Ariyachet ◽  
Supachaya Sriphoosanaphan ◽  
Nutcha Pinjaroen ◽  
Pongserath Sirichindakul ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Early Stage ◽  
High Sensitivity ◽  
Peripheral Blood Mononuclear ◽  
Diagnosis And Prognosis ◽  
Transcription Profiles ◽  
Blood Mononuclear Cells

AbstractNovel and sensitive biomarkers is highly required for early detection and predicting prognosis of hepatocellular carcinoma (HCC). Here, we investigated transcription profiles from peripheral blood mononuclear cells (PBMCs) of 8 patients with HCC and PBMCs from co-culture model with HCC using RNA-Sequencing. These transcription profiles were cross compared with published microarray datasets of PBMCs in HCC to identify differentially expressed genes (DEGs). A total of commonly identified of 24 DEGs among these data were proposed as cancer-induced genes in PBMCs, including 18 upregulated and 6 downregulated DEGs. The KEGG pathway showed that these enriched genes were mainly associated with immune responses. Five up-regulated candidate genes including BHLHE40, AREG, SOCS1, CCL5, and DDIT4 were selected and further validated in PBMCs of 100 patients with HBV-related HCC, 100 patients with chronic HBV infection and 100 healthy controls. Based on ROC analysis, BHLHE40 and DDIT4 displayed better diagnostic performance than alpha-fetoprotein (AFP) in discriminating HCC from controls. Additionally, BHLHE40 and DDIT4 had high sensitivity for detecting AFP-negative and early-stage HCC. BHLHE40 was also emerged as an independent prognostic factor of overall survival of HCC. Together, our study indicated that BHLHE40 in PBMCs could be a promising diagnostic and prognostic biomarker for HBV-related HCC.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

scholarly journals Quantitative Analysis of Inclusion Engineering on the Fatigue Property Improvement of Bearing Steel

Metals ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 476 ◽  
Author(s):  
Chao Gu ◽  
Min Wang ◽  
Yanping Bao ◽  
Fuming Wang ◽  
Junhe Lian
Keyword(s):  
Fatigue Life ◽  
Quantitative Analysis ◽  
Critical Size ◽  
Fatigue Behavior ◽  
Fatigue Cracks ◽  
Fatigue Property ◽  
Bearing Steel ◽  
Fatigue Properties ◽  
Bearing Steels ◽  
Inclusion Distribution

The fatigue property is significantly affected by the inner inclusions in steel. Due to the inhomogeneity of inclusion distribution in the micro-scale, it is not straightforward to quantify the effect of inclusions on fatigue behavior. Various investigations have been performed to correlate the inclusion characteristics, such as inclusion fraction, size, and composition, with fatigue life. However, these studies are generally based on vast types of steels and even for a similar steel grade, the alloy concept and microstructure information can still be of non-negligible difference. For a quantitative analysis of the fatigue life improvement with respect to the inclusion engineering, a systematic and carefully designed study is still needed to explore the engineering dimensions of inclusions. Therefore, in this study, three types of bearing steels with inclusions of the same types, but different sizes and amounts, were produced with 50 kg hot state experiments. The following forging and heat treatment procedures were kept consistent to ensure that the only controlled variable is inclusion. The fatigue properties were compared and the inclusions that triggered the fatigue cracks were analyzed to deduce the critical sizes of inclusions in terms of fatigue failure. The results show that the critical sizes of different inclusion types vary in bearing steels. The critical size of the spinel is 8.5 μm and the critical size of the calcium aluminate is 13.5 μm under the fatigue stress of 1200 MPa. In addition, with the increase of the cleanliness of bearing steels, the improvement of fatigue properties will reach saturation. Under this condition, further increasing of the cleanliness of the bearing steel will not contribute to the improvement of fatigue property for the investigated alloy and process design.

Plastic Slip and Early Stage of Fatigue Damage in Polycrystals: Comparison between Experiments and Crystal Plasticity Finite Elements Simulations

2014 ◽  
Vol 891-892 ◽  
pp. 1711-1716 ◽  
Author(s):  
Loic Signor ◽  
Emmanuel Lacoste ◽  
Patrick Villechaise ◽  
Thomas Ghidossi ◽  
Stephan Courtin
Keyword(s):  
Fatigue Crack ◽  
Finite Elements ◽  
Crack Initiation ◽  
Fatigue Damage ◽  
Crystal Plasticity ◽  
Early Stage ◽  
Low Cycle Fatigue ◽  
Fatigue Cracks ◽  
Fatigue Crack Initiation ◽  
Plastic Slip

For conventional materials with solid solution, fatigue damage is often related to microplasticity and is largely sensitive to microstructure at different scales concerning dislocations, grains and textures. The present study focuses on slip bands activity and fatigue crack initiation with special attention on the influence of the size, the morphology and the crystal orientation of grains and their neighbours. The local configurations which favour - or prevent - crack initiation are not completely identified. In this work, the identification and the analysis of several crack initiation sites are performed using Scanning Electron Microscopy and Electron Back-Scattered Diffraction. Crystal plasticity finite elements simulation is employed to evaluate local microplasticity at the scale of the grains. One of the originality of this work is the creation of 3D meshes of polycrystalline aggregates corresponding to zones where fatigue cracks have been observed. 3D data obtained by serial-sectioning are used to reconstruct actual microstructure. The role of the plastic slip activity as a driving force for fatigue crack initiation is discussed according to the comparison between experimental observations and simulations. The approach is applied to 316L type austenitic stainless steels under low-cycle fatigue loading.

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