Abstract: In the present study, two TRIP-assisted steels were designated as A (having no Cr and Cu content) and B (having higher Ni, Cr and Cu content) heat treated under different conditions, and the correlation between its heat treatment, microstructure and properties were investigated. Micro structural examination was carried out by optical microscope and scanning electron microscope after electrolytic etching. Non-destructive electrochemical and ultrasonic testing on two TRIP-assisted steels was used to find out corrosion and mechanical properties of different alter microstructure phase’s steels. Furthermore, micro structural studies accompanied by the evaluation of mechanical properties revealed that steels having martensite phases with higher corrosive and hardness value were less sound velocity and also steel’s microstructure having finer grains that was more grain boundary was less corrosion resistance. Steel containing more Cu, Ni and Cr was less corrosive compared to other steels having same processing or microstructure.
Abstract: Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.
Abstract: This paper describes the use of artificial neural
networks (ANN) for predicting non-linear layer moduli of flexible
airfield pavements subjected to new generation aircraft (NGA)
loading, based on the deflection profiles obtained from Heavy
Weight Deflectometer (HWD) test data. The HWD test is one of the
most widely used tests for routinely assessing the structural integrity
of airport pavements in a non-destructive manner. The elastic moduli
of the individual pavement layers backcalculated from the HWD
deflection profiles are effective indicators of layer condition and are
used for estimating the pavement remaining life. HWD tests were
periodically conducted at the Federal Aviation Administration-s
(FAA-s) National Airport Pavement Test Facility (NAPTF) to
monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test
gear trafficking on the structural condition of flexible pavement
sections. In this study, a multi-layer, feed-forward network which
uses an error-backpropagation algorithm was trained to approximate
the HWD backcalculation function. The synthetic database generated
using an advanced non-linear pavement finite-element program was
used to train the ANN to overcome the limitations associated with
conventional pavement moduli backcalculation. The changes in
ANN-based backcalculated pavement moduli with trafficking were
used to compare the relative severity effects of the aircraft landing
gears on the NAPTF test pavements.
Abstract: Bio-electrical responses obtained from freshwater
sediments by employing microbial fuel cell (MFC) technology were
investigated in this experimental study. During the electricity
generation, organic matter in the sediment was microbially oxidized
under anaerobic conditions with an electrode serving as a terminal
electron acceptor. It was found that the sediment organic matter
(SOM) associated with electrochemically-active electrodes became
more humified, aromatic, and polydispersed, and had a higher average
molecular weight, together with the decrease in the quantity of SOM.
The alteration of characteristics of the SOM was analogous to that
commonly observed in the early stage of SOM diagenetic process (i.e.,
humification). These findings including an elevation of the sediment
redox potential present a possibility of the MFC technology as a new
soil/sediment remediation technique based on its potential benefits:
non-destructive electricity generation and bioremediation.
Abstract: The Principal component regression (PCR) is a
combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the
use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and
implemented in the non-destructive assessment of the soluble solid
content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean
squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation
(RMSECV) of 0.8323 Brix° with principal components
(PCs) of 14.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: Agriculture products are being more demanding in
market today. To increase its productivity, automation to produce
these products will be very helpful. The purpose of this work is to
measure and determine the ripeness and quality of watermelon. The
textures on watermelon skin will be captured using digital camera.
These images will be filtered using image processing technique. All
these information gathered will be trained using ANN to determine
the watermelon ripeness accuracy. Initial results showed that the best
model has produced percentage accuracy of 86.51%, when measured
at 32 hidden units with a balanced percentage rate of training dataset.
Abstract: The X-ray technology has been used in non-destructive evaluation in the Power System, in which a visual non-destructive inspection method for the electrical equipment is provided. However, lots of noise is existed in the images that are got from the X-ray digital images equipment. Therefore, the auto defect detection which based on these images will be very difficult to proceed. A theory on X-ray image de-noising algorithm based on wavelet transform is proposed in this paper. Then the edge detection algorithm is used so that the defect can be pushed out. The result of experiment shows that the method which utilized by this paper is very useful for de-noising on the X-ray images.
Abstract: Science and technology of ultrasonic is widely used in
recent years for industrial and medicinal application. The acoustical
properties of 2-mercapto substituted pyrimidines viz.,2- Mercapto-4-
(2’,4’ –dichloro phenyl) – 6-(2’ – hydroxyl -4’ –methyl-5’ –
chlorophenyl) pyrimidine and 2 –Mercapto – 4-(4’ –chloro phenyl) –
6-(2’ – hydroxyl -4’ –methyl-5’ –chlorophenyl) pyrimidine have been
investigated from the ultrasonic velocity and density measurements at
different concentration and different % in dioxane-water mixture at
305K. The adiabatic compressibility (βs), acoustic impedance (Z),
intermolecular free length (Lf), apparent molar volume(ϕv) and
relative association (RA) values have been calculated from the
experimental data of velocity and density measurement at
concentration range of 0.01- 0.000625 mol/lit and 70%,75% and 80%
dioxane water mixture. These above parameters are used to discuss
the structural and molecular interactions.
Abstract: Significant changes in oil and gas drilling have
emphasized the need to verify the integrity and reliability of drill
stem components. Defects are inevitable in cast components,
regardless of application; but if these defects go undetected, any
severe defect could cause down-hole failure.
One such defect is shrinkage porosity. Castings with lower level
shrinkage porosity (CB levels 1 and 2) have scattered pores and do
not occupy large volumes; so pressure testing and helium leak testing
(HLT) are sufficient for qualifying the castings. However, castings
with shrinkage porosity of CB level 3 and higher, behave erratically
under pressure testing and HLT making these techniques insufficient
for evaluating the castings- integrity.
This paper presents a case study to highlight how the radiography
technique is much more effective than pressure testing and HLT.
Abstract: Dielectric sheet perturbation to the dominant TE111
mode resonant frequency of a circular cavity is studied and presented
in this paper. The dielectric sheet, placed at the middle of the airfilled
cavity, introduces discontinuities and disturbs the configuration
of electromagnetic fields in the cavity. For fixed dimensions of cavity
and fixed thickness of the loading dielectric, the dominant resonant
frequency varies quite linearly with the permittivity of the dielectric.
This quasi-linear relationship is plotted using Maple software and
verified using 3D electromagnetic simulations. Two probes are used
in the simulation for wave excitation into and from the cavity. The
best length of probe is found to be 3 mm, giving the closest resonant
frequency to the one calculated using Maple. A total of fourteen
different dielectrics of permittivity ranging from 1 to 12.9 are tested
one by one in the simulation. The works show very close agreement
between the results from Maple and the simulation. A constant
difference of 0.04 GHz is found between the resonant frequencies
collected during simulation and the ones from Maple. The success of
this project may lead to the possibility of using the middle loaded
cavity at TE111 mode as a microwave non-destructive testing of solid
materials.
Abstract: Non-Destructive evaluation of in-service power
transformer condition is necessary for avoiding catastrophic failures.
Dissolved Gas Analysis (DGA) is one of the important methods.
Traditional, statistical and intelligent DGA approaches have been
adopted for accurate classification of incipient fault sources.
Unfortunately, there are not often enough faulty patterns required for
sufficient training of intelligent systems. By bootstrapping the
shortcoming is expected to be alleviated and algorithms with better
classification success rates to be obtained. In this paper the
performance of an artificial neural network, K-Nearest Neighbour
and support vector machine methods using bootstrapped data are
detailed and shown that while the success rate of the ANN algorithms
improves remarkably, the outcome of the others do not benefit so
much from the provided enlarged data space. For assessment, two
databases are employed: IEC TC10 and a dataset collected from
reported data in papers. High average test success rate well exhibits
the remarkable outcome.
Abstract: The non-destructive testing of launch tube weld with
radiography was investigated and evaluated with AWS D1.1
standard. The paper started with preparation of launch tube and
radiographic inspection. X-Ray inspection then was done and gotten
the result. The judgment of inspection results were concluded by
certified person and finally, the evaluation with AWS D1.1 standard
was conducted as well.
The result shown that weld position P1 was not conformed to
AWS D1.1 which allowed size of incomplete penetration did not
exceed 4 mm. The other welds were corresponded to as mentioned
standard. Additionally, the corrective actions for incomplete
penetration either provided for future actions.
Abstract: This paper presents a method for determining the
uniaxial tensile properties such as Young-s modulus, yield strength
and the flow behaviour of a material in a virtually non-destructive
manner. To achieve this, a new dumb-bell shaped miniature
specimen has been designed. This helps in avoiding the removal of
large size material samples from the in-service component for the
evaluation of current material properties. The proposed miniature
specimen has an advantage in finite element modelling with respect
to computational time and memory space. Test fixtures have been
developed to enable the tension tests on the miniature specimen in a
testing machine. The studies have been conducted in a chromium
(H11) steel and an aluminum alloy (AR66). The output from the
miniature test viz. load-elongation diagram is obtained and the finite
element simulation of the test is carried out using a 2D plane stress
analysis. The results are compared with the experimental results. It is
observed that the results from the finite element simulation
corroborate well with the miniature test results. The approach seems
to have potential to predict the mechanical properties of the
materials, which could be used in remaining life estimation of the
various in-service structures.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.
Abstract: the work contains the results of complex investigation
related to the evaluation of condition of working blades of gas turbine
engines during fatigue tests by applying the acoustic emission
method. It demonstrates the possibility of estimating the fatigue
damage of blades in the process of factory tests. The acoustic
emission criteria for detecting and testing the kinetics of fatigue crack
distribution were detected. It also shows the high effectiveness of the
method for non-destructive testing of condition of solid and cooled
working blades for high-temperature gas turbine engines.