Pusat Pengajian Kejuruteraan Mekanikal - Tesis

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Low-cost condition monitoring for unbalanced motor systems using tuned dynamic vibration absorber
(2025-02)
Mohd Affan bin Mohd Rosli
Unbalanced motor is referred to the situation of a rotating system where there is an uneven distribution of mass, resulting the significant vibration or imbalance problems. Poor management of unbalanced motor can lead to various issues, such as increased vibration, decreased efficiency, and potential damage of both motor and the integrated system. This study investigates the performance of reduction vibration for an integrated unbalanced motor-beam structure using a Tuned Dynamic Vibration Absorbers (TDVA), with different types of TDVA stiffness (stainless steel, aluminium, brass and titanium). To gain a better nderstanding of the system dynamic behavior, the natural frequencies of the beam were determined using an Experimental Modal Analysis prior to implementing the TDVA. The Operational Deflection Shapes (ODS) experiment was conducted in the z-axis direction with three different motor speeds; 880 RPM (14.8 Hz), 2100 RPM (35 Hz) and 2800 RPM (46.5 Hz) to observe the most significant vibration of the beam during operation. Later, the TDVA which consisted of two secondary masses, was employed to modify the structural dynamic response of the beam. The lengths of the TDVA masses were adjusted based on the motor speed to optimize vibration reduction of the beam. The selection of TDVA stiffness materials was driven by their varying densities, moduli of elasticity and damping capacities, providing insight into their suitability for specific operating frequencies and conditions. Various TDVA stiffness materials were applied to determine the most effective vibration attenuation and it was found that aluminium material has produced the highest attenuation of 93.18 % at motor speed of 2880 RPM. Furthermore, a low-cost condition-based monitoring (CBM) system was developed using an Arduino Uno microcontroller connected to a Raspberry Pi. This system utilized an MPU9250 sensor which is cost-effective and appropriate for vibration measurement. The CBM system dashboard was hosted using the cloud, allowing real-time access to the vibration data. The system employed four programmable conditions to continuously assess the vibration activities. This affordable approach offers an accessible solution for small-scale industries, reducing reliance on expensive industrial-grade analyzers. In the event of abnormal vibration, the CBM system can trigger a notification alert, serving as a preventive measure against structure failures. The findings contribute to broader applications, including the improvement of maintenance strategies across various industries, emphasizing the transformative impact of combining effective vibration control and low-cost monitoring systems. It is also contributes to the understanding of the effect of different TDVA stiffness materials on the vibration control of beam structures with the additional of practical approach for real-time condition monitoring to improve system reliability.
Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations
(2014-05)
Goheannee
Semiconductor manufacturing industry in general has moved into high mix productions resulting from the drastic pace of product innovation. Capacity planning In semiconductor manufacturing facility, such as allocating right mix of products to maximize the capacity output, needs to consider multiple mutually influenced constraints in resource, product demand, as well as product and process characteristics. To achieve the best allocation, optimization methods, such as metaheuristic algorithms are commonly used. This research compares the performances of various metaheuristic algorithms to optimize tool capacity allocation in two case studies. In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. These algorithms are inspired by different nature of phenomenon. The former three are common in literature for tool capacity allocation problems. The latter three are the next generation of metaheuristic algorithms and albeit popular elsewhere, have no known attempt in tool capacity allocation problems. The case studies were obtained from two real industries and five demand scenarios were derived. The demand scenarios were with different demand intensities and levels. For each case study, a capacity model was constructed in Microsoft Excel spreadsheet, as an input to the above mentioned metaheuristic algorithms which programmed in Matlab coding. The performances considered are tool utilization and aggregate capacity outputs.
Ginger seed growth recognition using mask region based convolutional neural network (mask r-cnn)
(2023-01-01)
Tong Yin Syuen
As a plant that poses unique culinary and medical uses, ginger has emerged as a valuable commodity in Asia. Among the critical processes in the production of ginger is ginger seed preparation. It is particularly important to monitor the growth and quality of ginger seeds before they are being sown in growing media to ensure germination. However, to date, the ginger seed monitoring process remains manual and is reliant on human experts, despite the growing demand for more effective and accurate monitoring. In this work, a total 1,746 images consisting 2,230 sprout instances were collected from 282 ginger seed samples. In order to realize the automatic monitoring of ginger seeds, deep learning architectures were employed to detect the ginger seed sprouts in three stages from the digital images. This work assessed and compared the instance segmentation task using end-to-end Mask R-CNN models built by different strategies. Then, a two-stage hybrid detector-classifier model was also proposed to benefit from model task specialization concept. Specifically, an end-to-end binaryclass Mask R-CNN and multi-class classifier were combined to be compared to an end-to-end multi-class Mask R-CNN. The experimental results indicate that the use of the hybrid detector-classifier model developed in this work achieved mAP0.50 of 84.27% at inference time of 0.383 second per image in the detection of 402 images consisting of 514 sprout instances. Besides, substantial confusion between object classes in the model was also observed to be in line with the human expert’s perception in data annotation.
Combustion characterization and optimization of mixture biomass producer gas and methane in a constant volume combustion chamber system for fuel combustion efficiency enchancement
(2023-03-01)
Teh Jun Sheng
Most of the world’s energy requirements are still derived from natural resources. This will result in a catastrophic energy crisis with negative environmental consequences. The increased energy supply will result in greater consumption of non-renewable sources. The production of biomass producer gas (BPG) from biomass gasification has received significant attention for reducing global emissions as an alternative fuel because of the depletion of non-renewable resources. The properties of biomass feedstocks significantly influence combustion characteristics. The objective of this experimental study was to determine the combustion characteristics: flame propagation speed, chamber pressure trace and emissions of BPG at different equivalence ratio to obtain the lower chamber peak pressure and greenhouse gas emissions. Using the direct visualization technique, an optical constant volume combustion chamber (CVCC) was developed to measure combustion characteristics. Liquid petroleum gas (LPG) was used to compare chamber pressure and flame propagation speed in the CVCC calibration. In comparison to wood pellet (WP), coconut husk (CH), and palm kernel shell (PKS), the chamber peak pressure at 𝜙 equal to 1 of CH for the combustion of BPG is the lowest at 20.84 bar. At 𝜙 of 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, and 1.3, the chamber peak pressure of CH was discovered to be around 17.77, 18.12, 18.81, 20.84, 20.39, 17.25, and 16.37 bar. Compared to the other two types of BPG, CH produces the lowest emission of CO2 and CO, at 2.03% and 0.02%, respectively. From the literature review, increasing CH4 content in the fuel can increase the mole fraction of H, O2, and OH radicals and reaction rates in the flame, further accelerating the flame of the mixtures. Therefore, an optimization study is needed to determine the higher performance combustion of BPG with an increase in the composition of methane. The combustion experiment study was optimized with 17 designed experiments, 0.9 to 1.1 equivalence ratio, and the 0 to 0.1 mole fraction of methane fuel. The BPG-methane-air mixture, according to optimization analysis, achieves the fastest flame propagation speed and the lower chamber peak pressure at 𝜙 equal to 1 and a mole fraction of methane fuel of 0.083. Compared to the BPG-air mixture (𝜙 equal to 1), which had a chamber peak pressure of 20.84 bar, the average results of the optimum configuration parameters reveal a lower peak pressure was 18.97 bar. Comparison of the chamber peak pressure between BPG-methane-air mixture and BPG-air mixture varied by approximately 9.39%. In this context, the gross heat release rate (HRR) is observed to be around 94.44 kW, which represents a 20% reduction when compared to CH fuel. However, there is a slight increase in the emissions of CO and CO2, with a rise of 0.01% and 5%, respectively. In conclusion, the optimal mixture of BPG and methane fuel provides the optimum flame propagation speed with lower chamber peak pressure than BPG.
Integration of through-the-road parallel architecture hydraulic hybrid vehicle
(2023-07-01)
Tan Pe Hao
This study focuses on the installation of the hydraulic hybrid drive train into a conventional vehicle and fuel economy performance of the Through-The-Road (TTR) Hydraulic Hybrid Vehicle (HHV). TTR is a type of parallel hybrid architecture that connects the conventional drive train and the hybrid drive train via road. The hydraulic hybrid drive train that was previously on a test rig was installed into the vehicle to be road-tested. The fuel economy is compared by testing the vehicle with and without the hydraulic hybrid mode. Different tests were conducted including acceleration and deceleration test as well as drive cycle test. From the acceleration and deceleration test, the optimum charging pressure was determined as 140 bars, where the acceleration test shows 7.8% improvement in fuel economy while comparing to the conventional vehicle. The drive cycle test at 100 bars charging pressure shows hydraulic Hybrid On mode provides 8% fuel economy improvement comparing to the Hybrid Off mode

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