Pusat Pengajian Kejuruteraan Mekanikal - Tesis

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Development of a triaxial biomechanical force plate based on uniaxial load cells and deep learning
(2024-08-01)
Yeo, Ying Heng
Custom force plate developed from half-bridge strain gauge load cells is a potential low-cost alternative to expensive laboratory-grade force plates. Nevertheless, the measurement accuracy has not been thoroughly validated. The inability to quantify bilateral ground reaction force (GRF) prevents the utilization of the low-cost force plate in biomechanical analysis. In this study, a low-cost custom force plate has been developed by using uniaxial half-bridge strain gauge load cells. Based on Poisson effect, the load cells could produce readings even when the off-axis bilateral GRF which was orthogonal to the primary axis vertical GRF was applied. These readings were used as features to infer the bilateral GRF measured with laboratory-grade force plate. The mapping of uniaxial load cell readings to bilateral GRF was carried out using deep learning models. The validity of the custom force plate in measuring three-dimensional GRF, centre of pressure (CoP), and clinical metrics derived from vertical GRF and CoP was evaluated. The custom force plate validity in vertical GRF and CoP measurement for all tasks was indicated by mean absolute error of lower than 9.90 N and 6.29 mm, and high Pearson correlations (ρ), coefficient of determinations (R2), and intraclass correlation coefficients (ICC) of more than 0.94, 0.88, and 0.94 respectively. In acquiring clinical metrics, the custom force plate achieved ρ, R2, and ICC of greater than 0.98, 0.96, and 0.98 respectively. The recorded ρ and ICC were higher than that achieved in five previous studies which investigated other low-cost force plates. Autoencoder and U-net models were trained to receive time series or Short-Time Fourier Transformed (STFT) vertical GRF (acquired from the individual single-axis load cells of a custom force plate) as input and generate bilateral GRF as output. Different models were trained with Adam optimizer under the implementation of early stopping and hyperparameter tuning. The most accurate model was U-net model that accepted STFT-transformed input. Apart from the mediolateral GRF measured during sit-to-stand, the model predicted the bilateral GRF in the test dataset with root mean squared error (RMSE), and relative RMSE of less than 1.95% of body weight and 14.17%, and ρ, R2, and ICC of more than 0.89, 0.79, and 0.88, respectively. The values of ρ were greater than that obtained in six previous works that studied the bilateral GRF prediction methods with devices other than low-cost force plate. The result comparison with previous works highlighted the good measurement performance of the custom force plate. Hence, the custom force plate could potentially be a low-cost solution to measure GRF, CoP, and clinical metrics.
Feasibility of 3d scanner based on infra-red ranging sensor
(2024-08-01)
Tian, Kailai
In an era marked by rapid advancements in science and technology, the proliferation of digital and electronic products has greatly enhanced everyday convenience. Within this context, the 3D scanning market presents significant opportunities for development and application in various fields. Current market offerings of traditional laser 3D scanners and photogrammetry-based 3D scanners commonly face limitations due to lighting conditions. Scanning outcomes in outdoor or bright light environments are often suboptimal. Furthermore, traditional laser 3D scanners require the application of a developer for scanning objects with high refractive or transmittance rates, such as glass products or metal materials. This requirement poses restrictions for objects with fragile surfaces or those under cultural heritage conservation. The study evaluates the strengths and weaknesses of current 3D scanning methods, including camera-based and radar-based scanners, with the goal of creating a scanning approach that improves data acquisition speed and offers a universal, streamlined structure for daily use. The research introduces a novel 3D scanning sensor that leverages infrared ranging and point cloud processing to overcome the limitations of traditional photo-based scanners, which require time-consuming captures from multiple angles. By employing infrared ranging and point cloud analysis, our approach accelerates the data collection process. We designed and tested a 3D scanning system utilizing infrared sensors, proposing the elimination of complex rotating mechanical components in favour of a simpler, more user-friendly design. A key feature of our research is the use of cost-effective and stable sensors, significantly reducing the overall cost. This system solves the problem of poor scanning results of traditional laser 3D scanners and camera-based 3D scanners outdoors or in strong light conditions. The error between outdoor and indoor point cloud results is only 1.7%. At the same time, it also solves the problem that laser 3D scanners need to spray developer for objects with high refractive index or light transmittance. The data reliability of wooden scans using this system is 94.63%. This overall cost of this system is less than 2% of the price of the EinScan-S3D scanner. If a higher-precision infrared scanner is used to improve the accuracy of this design system, the price is still less than 50% of the price of the EinScan-S3D scanner. This highlights the practicality and affordability of our proposed 3D scanning sensor, making it a viable solution for everyday applications
Numerical study of heat transfer and flow characteristics of microchannel heat sink with staggered water droplet geometries using water and nanofluids
(2024-02)
Soo, Yan Hao
The rapid transition of electronic devices from low performing, low-speed systems to high performance systems with high computational speeds has led to the exponential hike in power density which poses a challenge for effective heat dissipation. To prevent thermal-induced damages in miniaturized electronic devices, the heat dissipation rate must be increased by incorporating heat exchangers with large surface area to volume ratio such as the microchannel heat sink (MCHS). This research sought to augment the thermohydraulic performance of a conventional MCHS using two different approaches: geometrical modification and flow parameter modification. Three-dimensional conjugate heat transfer analyses were conducted using state of the art computational fluid dynamics (CFD) software, ANSYS Fluent 2022, to assess the hydrothermal attributes of water and water-based nanofluids on an MCHS, employing staggered water-droplet geometries. The research focused on single phase laminar flow (Re<1000) through microchannels with hydraulic diameter (D_h) of 750 μm and aspect ratio of 1.5. The thermohydraulic performances of the MCHS were evaluated against different geometric parameters including groove aspect ratio, groove pitch, groove size, and geometry type. Apart from that, the impacts of the nanofluid properties (i.e., nanoparticle type and nanoparticle concentration) on the heat dissipation performance were also examined in the study. The geometrical modifications were found to induce a substantial enhancement in the thermal performance of the MCHS through the promotion of fluid mixing which intensified the thermal exchange between the solid and fluid domains. However, the pumping cost associated with such modifications were higher because of a higher pressure drop penalty. The employment of nanoparticles as fluid additives led to a substantial heat transfer increment, albeit with a higher pumping power requirement. With that being said, the implementation of nanofluids is preferable in comparison to plain water as the advantages greatly outweigh the disadvantages.
Performance characterization of bag-valve-mask (BVM) compression using machine learning
(2024-02)
Sanjivan Muthu Kumar
Medical staff face issues when ventilating patients manually using the Bag-Valve-Mask (BVM) for long periods to resuscitate patients unable to breathe properly on their own. As for ICU mechanical ventilators in hospitals, medical specialists must check on patients frequently and adjust settings manually. Currently, there are portable ventilators available in the market that aid in supplying oxygen to patients, however the usage of ML is rare, and they do not take into account various variables which are deemed important in patient recovery. In this research, the BVM was used to perform ventilation using manual and automated methods, after which machine learning (ML) study was done. The first objective was to predict the average tidal volume using artificial neural network (ANN) and boosted decision tree regression algorithms. The R2 value obtained from manual ventilation using ANN was 0.738861, whereas the boosted decision tree model scored 0.600049. Thus, ANN was used on the automated ventilation system to compare its performance with the manual, where an R2 value of 0.978604 was obtained after removing unwanted features. When compared with the manual model, a 32% increase in R2 was obtained. K-fold cross validation was carried out to test the manual and automated models in a bigger data space, where the standard deviation of the automated model was significantly lower, indicating lower variability within its dataset. The outcome of the study suggests that the automated system predicts the experiment data better than the manual system when utilizing ANN. Another objective of this research included conducting ML study using data collected from an ICU mechanical ventilator to provide a setting recommendation for a particular patient using linear and Poisson regression, where linear regression scored a R2 value of 0.936, whereas the Poisson model scored 0.836 when tested on tidal volume (TV) setting. Thus, linear regression was used to perform ML on the TV setting, fraction of inspired oxygen (FiO2) setting, and positive end-expiratory pressure (PEEP) setting, where TV setting scored the highest R2 values overall. To validate the TV setting formula obtained through Microsoft (MS) Azure, three experiments were conducted using a ventilator prototype on an artificial test lung for validation. The experiments yielded error results ranging from 53% to 79%, indicating that the TV setting values obtained from the prototype were incomparable to mechanical ventilator data. Extensive research is needed to compare the results between BVM ventilators and ICU mechanical ventilators.
Optimization of wire-bonding process parameters for gold wire and aluminium substrate using response surface methodology
(2024-09-01)
Megat Sufi Aniq, Mohamad Rosli
Wire bonding is a connecting technique that uses a combination of temperature, force, ultrasonic power, and time to attach two metallic materials, a wire, and a bond pad. In electronics, gold-aluminium (Au-Al) contact wire bonds are widespread due to corrosion resistance and great conductivity of both metals. However, the intermetallic compound growth at the metal’s interaction boundary has considerably worsened the advantageous properties of Au-Al intermetallic system due to the large contact- potential difference. These intermetallics exhibit low toughness, and possibly low corrosion resistance, which would result poor bonding quality. This study aims to investigate the most optimized set of parameters to be used in Au-Al wire bond system. During the initial study, this research’s starting point is to identify the dependent and independent parameters within the wire bonding process by using the two fractional factorial method. After conducting the experiments, data has shown that there are 4 dependent and 4 independent parameters. The four dependent parameters are time of bond at first bonding site, wire looping height, Y-axis length and ultrasonic force at second bonding site. These four dependent parameters are brought into extended study for further analysis. In the extended study, the experimental array was created using a response surface methodology (RSM)-based design of experiments. The effect of parameters and their significance to bonding quality in the Au-Al bond system were studied using analysis of variance (ANOVA). A correlation model was created for the wire bond strength data. The findings suggest that, within the range of parameters examined, the proposed correlation model can be utilized to predict performance measures. The optimum value of Au-Al wire bond system parameters was established at time of bond at first bonding site selected for 300 milliseconds (ms), wire looping height selected for 1200 micrometre (μm), wire bond Y-axis length selected for 996 micrometre (μm), and ultrasonic force at second bonding site selected for 300 milli Newton (mN). A validation test was conducted to verify the adequacy of the developed regression model, and the percentage errors between the predicted and experimental data were calculated as 0.91% to 7.05%. Thus, the regression model created for this study are capable of being reasonable accurate. The outcomes of this research add to our understanding of the Au-Al wire bonding contact for the engineers in the microelectronics industry and to enhance the wire bond’s quality during the electronic packaging process.

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