scholarly journals Infrared Thermography Approach for Pipelines and Cylindrical Based Geometries

Polymers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1616
Author(s):  
Saed Amer ◽  
Houda Al Zarkani ◽  
Stefano Sfarra ◽  
Mohammed Omar
Keyword(s):  
Infrared Thermography ◽  
Signal To Noise Ratio ◽  
Mean Shift ◽  
Fine Tuning ◽  
Complex Geometries ◽  
Defect Depth ◽  
Taguchi Design Of Experiment ◽  
Analysis Of Means ◽  
Testing Complex ◽  
3D Printed

Infrared thermography (IRT) is a competitive method for nondestructive testing; yet it is susceptible to errors when testing objects with complex geometries. This work investigates the effects of regulating different thermographic testing parameters to optimize the IRT outcomes when testing complex shaped geometries, particularly cylindrical coupons. These parameters include the scanning routine, feed-rate, and heat intensity. Fine-tuning these parameters will be performed with respect to three different variables consisting of workpiece density, defect size, and defect depth. The experimental work is designed around 3D-printed cylindrical coupons, then the obtained thermal images are stitched via image processing tool to expose defects from different scans. The analysis employs a Signal-to-Noise Ratio (SNR) metric in an orthogonal tabulation following a Taguchi Design of Experiment. Moreover, test sensitivity and the best combination of factor levels are determined using Analysis of Means (ANOM) and Analysis of Variance (ANOVA). The outcomes show that the heating intensity factor is the most dominant in exposing flaws with close to 40% mean shift and up to 47% variance fluctuation. The paper introduces the tools employed in the study, and then explains the methodology followed to test one sample quadrant. The results for running the testing on all the scenarios are presented, interpreted, and their implications are recommended.

scholarly journals A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Taili Du ◽  
Xusheng Zuo ◽  
Fangyang Dong ◽  
Shunqi Li ◽  
Anaeli Elibariki Mtui ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Work Conditions ◽  
Copper Electrode ◽  
Vibration Sensor ◽  
High Signal ◽  
Triboelectric Nanogenerator ◽  
Vibration Exciter ◽  
Bouncing Ball ◽  
3D Printed ◽  
Self Powered

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

Predictive Modeling of Polymer Saturation in Porous 3D Printed Matrix

2019 ◽  
Author(s):  
Adam Mihalko ◽  
Robert Michael ◽  
Davide Piovesan
Keyword(s):  
Additive Manufacturing ◽  
Physical Properties ◽  
Composite Ceramic ◽  
Medical Industry ◽  
Solid Matrix ◽  
Complex Geometries ◽  
Adhesive Binder ◽  
3D Printed ◽  
Binder Jetting ◽  
Accuracy Speed

Abstract Due to the accuracy, speed, and ability to produce controllable complex geometries, additive manufacturing has gained traction in the medical industry. Additive manufacturing based on powder binder-jetting allows fabricating composite ceramic artifacts to mimic the physical properties of cortical bone. Given the porous nature of the artifacts their physical properties can be manipulated based on the percentage of solid matrix and adhesive binder. It has been demonstrated that a reduction of porosity via infiltration greatly increases the mechanical properties of the artifact. In this paper experiments are presented investigating the post processing of porous materials using different adhesives to infiltrate the artifact. The resulting saturation and porosity profiles of the produced composite are analyzed.

Advanced Signal Processing Techniques for Guided Wave Corrosion Monitoring System in Secondary Circuits of Nuclear Power Plants

2018 ◽  
Author(s):  
Andrei Gribok ◽  
Vivek Agarwal
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Signal To Noise Ratio ◽  
Guided Wave ◽  
Detection Sensitivity ◽  
Complex Geometries ◽  
Corrosion Monitoring ◽  
Coherent Noise ◽  
Induced Defects

This paper describes the application of independent component analysis (ICA) to detect corrosion-induced defects in commercial nuclear power plants. This paper analyzes the applicability and benefits of ICA when applied to guided wave (GW) technology to detect corrosion in secondary circuits, as well as studying the potential for expanding the range of GW technology to include complex geometries and piping components. The ultrasonic GWs can inspect long stretches of straight piping; however, more complex geometries that include elbows, welds, and tees are causing spurious reflections and coherent noise, which significantly decreases the sensitivity of the GW systems. The potential of ICA to improve detection sensitivity is analyzed and practical recommendations are provided. It is demonstrated on GW data collected at one of the commercial nuclear power plants that ICA, under certain conditions, is capable of separating different coherent noise components and has potential for improving signal-to-noise ratio.

scholarly journals Nano-3D-printed Photochromic Objects

2020 ◽  
Author(s):  
Sebastian Ulrich ◽  
Xiaopu Wang ◽  
Markus Rottmar ◽  
René M. Rossi ◽  
Bradley J. Nelson ◽  
...  
Keyword(s):  
Click Reaction ◽  
Thermal Recovery ◽  
Fine Tuning ◽  
Excitation Wavelength ◽  
Color Changes ◽  
New Class ◽  
Direct Laser ◽  
Donor Acceptor ◽  
3D Printed ◽  
Post Modification

A new class of photoresist is described for direct laser writing of photoswitchable 3D microstructures. The material comprising off-stoichiometric thiol-ene photo-clickable resins enables rapid two-photon laser processing of highly complex structures and facile post-modification with photoswitches. The microstructures were functionalized with a series of donor-acceptor Stenhouse adducts (DASAs) photoswitches with different excitation wavelength. The versatility of thiol–ene photo-click reaction enabled fine-tuning of the network structure and physical properties as well as the type and concentration of DASA photoswitches. When exposed to visible light, these microstructures exhibit excellent photo-responsiveness and undergo reversible color-changing via photoisomerization of DASA moieties. We describe that the weak fluorescence of DASAs can be used as a reporter of photoswitching, color changes, and thermal recovery, allowing the reading of DASA-containing sub-micrometric structures in 3D. This work delivers a new approach for custom microfabrication of 3D photochromic objects with molecularly engineered color and responsiveness.

scholarly journals 3D Printed Scaffolds for Monolithic Aerogel Photocatalysts with Complex Geometries

Small ◽  
2021 ◽  
pp. 2104089
Author(s):  
Murielle Schreck ◽  
Nicole Kleger ◽  
Fabian Matter ◽  
Junggou Kwon ◽  
Elena Tervoort ◽  
...  
Keyword(s):  
Complex Geometries ◽  
3D Printed

scholarly journals Consistency of Hearing Aid Setting Preference in Simulated Real-World Environments: Implications for Trainable Hearing Aids

2020 ◽  
Vol 24 ◽  
pp. 233121652093339
Author(s):  
Els Walravens ◽  
Gitte Keidser ◽  
Louise Hickson
Keyword(s):  
Hearing Aids ◽  
Real World ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Traffic Noise ◽  
Fine Tuning ◽  
Signal To Noise ◽  
Acoustic Environment ◽  
Change Environment ◽  
Noise Ratio

Trainable hearing aids let users fine-tune their hearing aid settings in their own listening environment: Based on consistent user-adjustments and information about the acoustic environment, the trainable aids will change environment-specific settings to the user’s preference. A requirement for effective fine-tuning is consistency of preference for similar settings in similar environments. The aim of this study was to evaluate consistency of preference for settings differing in intensity, gain-frequency slope, and directionality when listening in simulated real-world environments and to determine if participants with more consistent preferences could be identified based on profile measures. A total of 52 adults (63–88 years) with hearing varying from normal to a moderate sensorineural hearing loss selected their preferred setting from pairs differing in intensity (3 or 6 dB), gain-frequency slope (±1.3 or ± 2.7 dB/octave), or directionality (omnidirectional vs. cardioid) in four simulated real-world environments: traffic noise, a monologue in traffic noise at 5 dB signal-to-noise ratio, and a dialogue in café noise at 5 and at 0 dB signal-to-noise ratio. Forced-choice comparisons were made 10 times for each combination of pairs of settings and environment. Participants also completed nine psychoacoustic, cognitive, and personality measures. Consistency of preference, defined by a setting preferred at least 9 out of 10 times, varied across participants. More participants obtained consistent preferences for larger differences between settings and less difficult environments. The profile measures did not predict consistency of preference. Trainable aid users could benefit from counselling to ensure realistic expectations for particular adjustments and listening situations.

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