Pusat Pengajian Kejuruteraan Mekanikal - Tesis

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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
Synthesis of stretchable conductive polymer for electronics circuit application
(2023-04-01)
Sana Zulfiqar
Stretchable electronic circuits (SECs) have become very popular nowadays in various mechanical, electrical and biomedical engineering applications. They are comprised of flexible and stretchable substrate as well as conductive ink, and electronic components. The stretchability and flexibility of SECs can be controlled by the proper selection of materials and designs for the substrate and conductive ink. Moreover, the material used to develop the conductive ink must exhibit high electrical conductivity and good adhesion with the substrate to obtain a high quality of stretchable printed circuit. This study focussed on the synthesis, material modelling and the examination of various properties of polymeric substrate and conductive ink by different thermal, mechanical and electrical testing. For synthesis, PDMS-OH was used as a binder or elastomer in both the formulations and silver powder as a conductive filler for silver-based conductive ink. The mechanical properties of these materials were evaluated by simple UTM under tensile loading. The modulus of elasticity and tensile strength of the substrate and ink were found as 0.48 MPa and 2.18 MPa at 300% stretchability, and 5.72 MPa and 1.195 MPa with the yield stress of 0.86 MPa at 137% stretchability before rupture, respectively. Afterwards, the thermal analysis of the conductive ink was carried out by DSC and TGA. From DSC, the glass transition and melting temperatures of the cured ink were found as 130°C and 297.43°C, correspondingly. The thermal degradation was studied by TGA in which the weight loss occurred at different ranges of temperature. The residue of silver particles was obtained as 82.62% after complete analysis. This proves that the current formulation of the ink becomes more viscous at higher temperatures. Moreover, the storage modulus, loss modulus and damping ratio of the ink were calculated using DMA analysis. As a result, the silver ink exhibited low loss modulus value than the storage modulus, which proves that the current formulation of the ink was more elastic in nature rather viscous. Farther the micro mechanical analysis, the hardness and reduced modulus of the conductive were computed by nanoindentation technique. In addition, the surface analysis of the ink was done by OM and SEM. As a result, the silver particles were homogeneously spread throughout the surface of the ink. The electrical conductivity was measured by 2-point multi-meter before and after application of load. It was found as 1002 S/cm without loading, while, the resistance of the ink increased from 0.042 Ω to 25 Ω at 60% strain during loading and decreased from 25 Ω to 0.0767 Ω at 0% after unloading. Finally, the stress-strain data of respective material were utilized to characterize the material properties using hyper-elastic constitutive models for the substrate and multi-linear plastic models for the conductive ink. The curve fitting was done using three solvers, Abaqus, GRG and C-PSO algorithm. As a consequence, the Reduced Polynomial (𝑁=6) model under C-PSO algorithm was considered as the best fit hyper-elastic model than others. The validation of this hyper-elastic model was then executed through FE analysis. Consequently, the experimental results were in a good agreement with the simulated results.

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