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Optimization of process parameters on surface roughness and material removal rate of stainless steel AISI 316 in CNC milling process
No full textMaster of Science in Manufaturing System EngineeringThe CNC milling machine was used to machine the specimens made from stainless steel AISI 316 based on the selected parameters setting. Specimens with 6 mm in thickness obtaining was used. The material removal rate (MRR) was calculated by
dividing machine time the weight of the specimen weight before and after the cutting process. The second performance surface roughness (SR) and it was measured by MITUTOYO CS-3100 device. Taguchi Method was utilized as in layout the
experimental to optimize MRR, SR. It was found that prediction maximum value MRR was 4.86 mm3/s under setting spindle speed 2500 m/min , feed rate 250 mm/min , and depth of cut 0.1 mm . On the other hand the prediction minimum SR value 2.85 μm, was under setting spindle speed 500 m/min, feed rate 250 mm/min, and depth of cut
0.2 mm. Confirmation tests were run to verify. The prediction it was found the
experimental results of MRR and SR for within 10% percentage of the prediction value
Analytical and experimental analysis of buck-boost DC-DC converter in photovoltaic (PV) maximum power point tracking application
No full textMaster of Science in Electrical Power EngineeringRenewable energy has emerged throughout the world including Malaysia. One of the renewable energy sources is solar energy where the energy from solar (sunlight) is converted into electrical energy. However, the electricity generated from Photovoltaic (PV) module cannot be guaranteed to operate at maximum power if the PV modules or array are connected directly to the load. To overcome this problem, a method called Maximum Power Point Tracking (MPPT) is normally utilized in PV systems to ensure maximum power extraction. MPPT method consists of a DC-DC converter that is connected in between the PV generator and the load. The operating point of the system is controlled by varying the duty cycle of the converter. In this project, a DC-DC Buck-boost converter is employed as the MPP tracker to ensure continuous extraction of maximum power from a PV system at various environmental conditions. This converter is selected based on its ability to match the PV module impedance independence of the load and irradiance. Simulation and experimental analyses on the developed Buck-boost DC-DC converter is performed at irradiance level of 100 W/m2 to 1000 W/m2. A CHROMA PV Simulator (Model: 62100H-600S) is used to emulate 4 PV modules (MSX-60) connected in series. EZDSP F28335 Digital Signal Processing (DSP) is employed as a controller to control the duty cycle of the converter. Results showed that the developed converter is able to track the MPP for all irradiance levels tested. The simulation and experimental results are almost similar. In addition, it is proven that Buck-boost converter is able to trace the MPP from 0 V to open circuit voltage, Voc. Hence it should be able to find the correct MPP even during the occurrence of partial shading (PS) conditions in which the correct MPP will not be at its normal range, i.e. 0.7-0.8 times Voc
Effect of rice straw fiber and Polyethylene glycol on polylactic acid/polyhydroxybutyrate-valerate blends
No full textMaster of Science in Biosystems EngineeringBiodegradable polymer is one of the alternatives that have potential to overcome serious
environmental problem and depletion of crude oil. The materials that have been used in
this study are polylactid acid (PLA), polyhydroxybutyrate-valerate (PHBV), rice straw
(RS) fiber and polyethylene glycol (PEG). PLA and PHBV are one of biopolymer that
can complement each other properties. The mixing process of these composites was
carried out using heated two-roll mill (DW5110). The blends between PLA and PHBV
slightly reduced tensile strength but increased Eb. Besides, it also increased the Young’s
modulus. The optimum ratio of the blends is 50% PLA with 50% PHBV. This study
also focused on the effect of RS fiber and PEG as plasticizer on PLA/PHBV blends.
The RS fiber content was varied from 5 to 25 wt%. 5 wt% PEG based on RS fiber
content was added. The addition of RS fiber into the blends reduced the mechanical
properties and thermal stability however the water absorption increased. The
incompatibility of RS fiber with PLA/ PHBV blends was proven by SEM micrographs.
PLA/PHBV blends show high modulus and high tensile strength but it is quite brittle. .
The goal of adding plasticizer to PLA/ PHBV blend with RS fiber is to enhance the
polymer elongation and to reduce the brittleness while maintaining optimum polymer
strength and stiffness. After the addition of PEG, the tensile strength reduced, while the
Eb and Young modulus increased. The amphiphilic properties of PEG slightly reduced
the water absorption. From FTIR spectra, it can be seen that the presence of PEG into
PLA/PBV blends filled with RS fiber reduced the –OH group intensity. Natural
weathering and soil burial test until 6 months were performed to determine the
biodegradability of the polymer and were confirmed by evaluating the mechanical,
morphological, carbonyl index and crystallinity. The addition of RS fiber had reduced
the mechanical strength but carbonyl index and crystallinity increased. The crack, pores
and fungus colonization was shown in SEM micrograph, indicated that the
biodegradation was more pronounced with the addition of RS fiber. The addition of
PEG also helps to increase the degradability by increasing the surface area for microbial
attack. In conclusion, the addition of RS fiber increased the degradability and the
presence of PEG as plasticizer improved the Eb between the blends
Classification of vision perception using EEG signals for brain computer interface
No full textMaster of Science in Mechatronic EngineeringPatients suffering from Motor Neuron Disease (MND) and semi-paralysis have
trouble to maneuver a conventional wheelchair independently. As a response, this
research was conducted whereby an individual’s visual perception can associate to
movement controls. The designed system could later on be integrated into an autonomous
wheelchair. The Brain Computer Interface (BCI) system would require the
Electroencephalography (EEG) signal to be recorded from the subject using Mindset24
EEG amplifier. Subsequently, the signals’ noise content was been analysed with analysis
of variance (ANOVA) whereby signal with high noise content was removed from the
samples. Then, spectral energy of different bands of EEG signal (θ, α, β1, β2, β3 and γ)
pertaining to an individual’s visual perception were extracted. Next, dimension reduction
was performed to select band features based on feature separability using Devijver’s
Feature Index (DFI) and Principle Component Analysis (PCA). Finally, neural network
models, namely, multi-layered perceptron (MLP), Elman Recurrent Neural Network
(ERNN) and nonlinear exogenous autoregressive model (NARX) have been designed to
as classifiers to determine the subject’s visual perception, with an average accuracy of
over 90%. Among the trained classifier, ERNN was chosen for it yielded a relatively
higher performance in the both the Locational Matching and Image Recognition
Paradigm in terms of classification accuracies (97.75% and 97.81% respectively).
Therefore ERNN is the most suitable classifier to be used for application of visual
perception to help MND patient navigate in a wheelchair
Reduced graphene oxide-multi walled carbon nanotubes hybrid material as electrode for DNA biosensor
No full textMaster of Science in Nanoelectronic EngineeringThis thesis presents a novel thin film of reduced graphene oxide-multiwalled carbon
nanotubes (rGO-MWCNTs) composites as a sensing film electrode for Deoxyribonucleic
acid (DNA) immobilization and hybridization detection. This project consisted of three
parts, which are the rGO-MWCNTs composite thin film preparation and characterization,
the device fabrication processes description, and followed by the DNA immobilization
and hybridization. In the first part, the thesis describes the graphene oxide preparation
from graphite powder using improved Hummers’ method. Whereas, the multiwalled
carbon nanotubes (MWCNTs) was functionalized through nitric acid oxidation process.
Chemical reduction process was used to obtain the reduced graphene oxide using
hydrazine as reduced agent. The MWCNTs, GO, and rGO-MWCNTs materials were
mechanically sprayed on the silicon dioxide (SiO2) surface of the device channel using
spray technique. Chitosan solution was mixed with the materials and sprayed on the
device surface in order to increase the viscosity of the materials and strengthen their
adhesion with the silicon dioxide surface by changing the surface characteristic from
hydrophobic to hydrophilic. The morphology of the rGO-MWCNTs composite thin films
were observed by field emission scanning electron microscope. The bonding of the rGOMWCNTs
were examined using Fourier transform infrared spectroscopy. The phase
structure of the materials were confirmed via X-ray powder diffraction. Secondly, the
design, fabrication and evaluation of the device were descripted in details. In addition,
the device fabrication processes contained of oxidation process for silicon dioxide layer
growing, physical vapor deposition process which was used to deposit an aluminum layer
on the silicon substrate to form the source and drain, mask designed, printed, and utilized
in the pattern transfer process, and photolithography process which was carried out to
create the channel of the device. The operation of the electrode is based on the surface
charge adsorption of the film material interface. Finally, in the DNA immobilization and
hybridization section where the novelty of the research introduced, the biosensor
demonstrated high sensitivity to the complementary DNA target with a linear range from
500 pM to 100 pM. Furthermore, the biosensor demonstrated good selectivity,
reproducibility, and long-term stability for DNA detection. The device has shown
sufficient capability to distinguish between targets complementary DNA and different
DNA sequences, such as non-complementary and single-mismatched DNA. The
hybridization process of the non-complementary DNA has the smallest response (39 μA)
due to the double standard DNA was not effectively formed. Whereas, the singlemismatched
DNA has shown less response (55 μA) comparing with the complementary
DNA (65 μA) due to the single mismatched base. The device accuracy was investigated
and found to be 11.28 %. Since, the biosensor responded very well and demonstrated
excellent detection capabilities, it is highly recommended to be used in detecting specific
biomarkers and other targeted proteins