Pusat Pengajian Kejuruteraan Mekanikal - Monograf
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- PublicationReal-time diagnostics of centrifugal pump with Raspberry PI(2024-07-12)Emil Joseph DonsiaThis research project focuses on the development of real-time diagnostics of centrifugal pumps with Raspberry Pi. The Raspberry Pi is used as the DAQ unit which serves to collect data, preprocess data, handle data, and transmit data. By using the Raspberry Pi to interface the MPU6050 sensor, raw data from the centrifugal pump can be obtained which then undergoes preprocessing to extract the statistical features of both the time domain and frequency domain using FFT. Once the raw data is preprocessed, it can then be handled and transmitted for further analysis, therefore, enabling the Raspberry Pi to be a DAQ unit for this project. By collecting, preprocessing, and handling the raw data, a dataset can be prepared which is then utilized to train the ML model using Python in Jupyter Notebook. The SVM algorithm is utilized train to train the ML model using appropriate techniques to construct a reliable model with great predictive capabilities. Node-RED is utilized to build the IoT application by creating flows that handle incoming live data and employing the trained ML model to make predictions. Additionally, Node-RED is also used to construct the dashboard to visualise real-time monitoring and diagnostics of an operating centrifugal pump. In conclusion, this research project successfully employs the Raspberry Pi as a DAQ unit, developing an ML model with accurate predictive capabilities and an IoT system comprising the pump, DAQ, and Node-RED. By providing engineers with real-time diagnostic information and prognostic insights, the system contributes to the field of pump diagnostics by enhancing the efficiency of maintenance planning and reducing equipment failure rates. The project’s main achievements lie in the utilization of Raspberry Pi as a DAQ unit, the development of an accurate ML model for pump diagnostics, and the integration of the trained model into Node-RED for creating an IoT application for pump diagnostics and monitoring.
- PublicationHeat dissipation enhancement using graphene coating on plate-fin heat sink(2024-07)Desmond, Saw Jia-JuinThis study explores the application of graphene coatings on plate-fin heat sinks to improve heat dissipation in electronic devices. As electronic components become smaller, denser, and more powerful, efficient heat management is crucial for optimal device performance. Traditional heat sink designs face limitations due to increasing thermal demands. To address this challenge, the project introduces an innovative approach by integrating graphene coatings on plate-fin heat sinks. The research aims to determine the optimal graphene coating thickness at different regions on the heat sink and evaluate the heat dissipation enhancement achieved. Numerical simulations using ANSYS FLUENT 2022 R2 are conducted to investigate the impact of graphene coatings on heat transfer efficiency at various region under varying inlet air flow rates and flow directions. The results are discussed in terms of thermal resistance, average Nusselt number, pressure drop, and pumping power. The results show that higher mass flow rates improve heat dissipation performance for all coatings at all regions. The results also show that graphene coating at bottom region has minimal effect in improving the heat dissipation. In fact, thicker coating will affect the thermal dissipation performance. Graphene coated at the inner region has the most improvement in heat dissipation performance than side region, but it comes in a cost of increasing pumping power. The optimum graphene coating thickness for the best thermal performance is 0.5 mm at the bottom, 2 mm at the side, and 1.2 mm at the inner region, with an improvement of approximately 50 % in terms of thermal resistance as compared with the non-coated heat sink. Parallel flow direction will have a better heat dissipation performance as compared with impinging flow at the same Reynolds number. This research fills a critical knowledge gap by exploring the potential of graphene coatings to significantly improve heat dissipation in plate-fin heat sinks. Overall, this study not only addresses the pressing need for enhanced heat dissipation in electronic systems but also offers insights into the future of thermal management solutions.
- PublicationFinite element analysis and surface characterization of spur gear(2024-07-11)Deniswaran a/l SivaramanGears are fundamental components in mechanical systems, where any malfunction can severely affect overall performance. Effective lubrication methods are essential for reducing friction, wear, and potential damage to gear teeth, thereby preventing catastrophic failures. This study focuses on the finite element analysis (FEA) and surface characterization of spur gears to understand their stress distribution, deformation, and failure mechanisms, with an aim to enhance gear performance and longevity through optimized design and effective lubrication strategies. The primary objectives of this research are to investigate the stress distribution on the gear tooth surface using FEA and to perform a failure analysis on worn gears through surface characterization techniques such as Scanning Electron Microscopy (SEM) and Laser Scanning Microscope (LSM). The gear model is designed using SOLIDWORKS, and ANSYS software is employed for the simulation. The resulting contact stress data from FEA is compared with experimental data to validate the simulations. Results indicate that stress concentrations are predominantly located at the gear tooth root and tip, highlighting critical areas for potential failure. Surface characterization reveals significant wear patterns and roughness, underscoring the importance of proper lubrication in maintaining gear integrity. The gear lubricated with palm oil shows the lowest surface wear indicated by low roughness and less surface damage. By integrating FEA with detailed surface analysis, the study provides comprehensive insights into the mechanical behavior and surface integrity of spur gears, essential for their optimal design and improved operational efficiency.
- PublicationMachine learning study of void detection using machine learning(2024-07)Chew, Cheng KaiAs BGA chips continue to shrink in size, voiding has become a significant challenge in soldering, particularly during underfilling. Manual inspection for voids is not only labour-intensive but also prone to human error, making it difficult to accurately evaluate the void percentage for acceptance. This research introduces a deep learning CNN network image processing model, Mask R-CNN, developed using Detectron2, to detect voids in TSAM images of chips with epoxy underfill. Various datasets and ratio were compared using evaluation metrics to determine the best performance. Additionally, comparison between Roboflow training and Mask R-CNN model is conducted as well as evaluations between different Mask R-CNN. A regression study of the correlation between underfill encapsulation parameters and void percentage was conducted using Orange Data Mining and Minitab Statistical Software. The result is the highest mAP value of 0,537 achieved by 70-image dataset with ratio of 8:1:1. The Roboflow training shows overall higher mAP value compared to Mask R-CNN training with the highest mAP achieved at 70-image dataset, 0.584. The highest mAP value of 0.566 achieved by X101_FPN model shows the best performance while lowest time achieved by R101_FPN model shows the fastest training with 25.49 minutes. The regression study result shows valve pressure is the most significant parameters affect the void percentage of the chip.
- PublicationComparative analysis of solar charging station efficiency for electric two-wheelers versus conventional grid charging(2024-07-12)Bee, Chun ShengTransportation sector has been the third largest contributor to Greenhouse Gas emissions in Malaysia, leading to finding the alternative way to solve the current issues. The rising in adoption of electric two-wheeler as a solution is needed to be evaluated to find the best efficient and environmentally friendly way for charging the electric two-wheeler. This research aims to create a comprehensive analysis of solar charging station efficiency versus conventional grid charging for electric two-wheelers. The study aims to analyse the effect of both charging methods on the other aspects such as energy efficiency, environmental impact, energy consumption, cost evaluation, and the operational sustainability. The problem statement involves designing a simple solar PV system capable of charging the battery efficiently and recording various parameters for further investigation on its performance. For methodology, besides creating a simple solar PV system, a simple connection using the conventional charger connected to the power socket is created to have a better comparison between two methods. Throughout the whole research, the solar charging method are found to be the better option as it generates lesser greenhouse gas emissions through its lifecycle. The investment in installing the solar PV system in residential area also found to be valuable as the annual savings from the system can help in tariff the electricity bills, covering the cost of purchasing electric two-wheeler and making the further energy consumption from the electric two-wheeler to be free. However, the high initial installation cost becomes the biggest challenge in widespread the adoption. At the end of the research, it offers valuable insights for every authority, highlighting the advantages and limitations of transitioning to solar-powered charging infrastructure for electric two-wheelers.