Mohd Sazli Saad
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Preferred name
Mohd Sazli Saad
Official Name
Mohd Sazli , Saad
Alternative Name
Saad, Mohd Sazli
Saad, M. Sazli
Saad, Mohd S.
Saad, M. Sazli
Main Affiliation
Scopus Author ID
57219520932
Researcher ID
Y-4444-2019

Research Output

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Power Generation Improvement using Active Water Cooling for Photovoltaic (PV) Panel

2021-01-01 , Mohamad Shukor Abdul Rahim , Mohammad Faridun Naim Tajuddin , Mohd Sazli Saad , Nalini C. , Edaris Z.L.B. , Hasanuzzaman M.

Photovoltaic (PV) cooling systems are commonly used to improve photovoltaic panels power generation and efficiency. Photovoltaic (PV) panels require irradiance to generate power, although increasing irradiance is often correlated with increasing temperature. These rapid increases of temperature in photovoltaic (PV) panels severely affect the power conversion operation. With a proper cooling process on its surface, a solar photovoltaic (PV) system can operate at a higher efficiency. This research aims to study the power improvement of active water-cooling on photovoltaic (PV) panels. A fixed minimum water flow of 5.80 l/min is sprayed onto the panel's front surface to reduce the temperature. The sprayed water created a thin water film and managed to reduce the temperature. Other than that, there is also reference photovoltaic (PV) panel, which is a panel without any cooling system. The outputs compared are the module temperature, maximum output power, open circuit voltage, and short circuit current. As the irradiance starts increasing, the panel temperature also begins to spike. However, with active water cooling, the temperature was able to be reduced by 37.67% during the day's hottest temperature. This reduction of temperature creates power improvement to the cooled panel up to 253W, compared to the reference panel output of only 223W. During the overheating of a photovoltaic (PV) panel, the open circuit voltage was found to be the most affected. This increase in power with active water cooling can potentially have a massive impact on large-scale photovoltaic (PV) panel installations.

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FDM Parameters Optimization for Improving Tensile Strength using Response Surface Methodology and Particle Swarm Optimization

2024-09-01 , Mohamad Ezral Baharudin , Mohd Sazli Saad , Mohd Zakimi Zakaria , Azuwir Mohd Nor , Ab Talib M.H.

Fused deposition modelling (FDM) is a popular 3D printing technique that uses a thermoplastic filament as the build material. In FDM 3D printing, tensile strength can be an issue because the layers of the object are built on top of each other, and if the layers do not adhere properly, the object can be weak and prone to breaking. Typically, this problem is caused by incorrect parameter settings. Hence, this study was then carried out to analyse and improve the printing quality in term of tensile strength of the printed part using the response surface methodology (RSM) and the particle swarm optimization (PSO) method. The effect of four input parameters such as layer height, printing speed, infill density, and print temperature was examined on the tensile strength of polylactic acid (PLA) standard samples ASTM D638-IV. The experimental design was performed using face-centred central composite designs (FCCD). The experimental data were statistically analysed to form a regression model of the tensile strength. This model was used to approximate the actual process. The optimization was performed using desirability analysis from RSM and PSO to search for the optimal parameter for maximum tensile strength. Experimental results showed that PSO outperformed RSM with a 1.52 % reduction in tensile strength. The maximum tensile strength obtained from PSO was about 39.069 MPa with the optimal process parameters of layer height of 0.30 mm, printing speed of 30.17 m/s, infill density of 79.72 %, and print temperature of 205.92 °C.

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Optimizing Surface Roughness of PLA Printed Parts using Particle Swarm Optimization (PSO)

2023-01-01 , Hadi Irazman H.N. , Mohd Sazli Saad , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria , Nor A.M. , As'arry A.

Fused Deposition Modelling (FDM) is an additive manufacturing-based rapid prototyping technology that has gained widespread attention due to its ability to produce complex geometries with relatively low cost and fast production time. However, the surface finish of the FDM printed parts can be adversely affected by the selection of input parameters, such as layer height, infill density, print temperature, etc. This study aims to investigate the impact of these parameters on surface roughness and optimize the FDM process to improve surface finish. Two optimization approaches were employed in the study to address this problem, namely the Response Surface Methodology (RSM) and the particle swarm optimization (PSO) method. The impacts of four factors, layer height, printing speed, infill density, and print temperature, on the surface roughness of Polylactic Acid (PLA) printed parts were evaluated. A Face-centred Central Composite Design (FCCD) was used to reduce the number of experiments and to optimize the process. Both RSM and PSO methods were employed to find the best combination of process parameters for minimum surface roughness. The results of the experiment indicated that the optimal settings for minimum surface roughness were a layer height of 0.10 mm, printing speed of 30.36 m/s, infill density of 77.10 %, and print temperature of 195.12 °C, resulting in a surface roughness value of 1.31 µm. From these findings, the PSO optimization method was found to be more effective than the RSM method, showing a significant improvement in surface roughness with a reduction of 13.5 %.

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Optimizing Fused Deposition Modeling with ANN: Material Consumption and Tensile Strength Predictions

2023-01-01 , Nasuha H. , Mohd Sazli Saad , Mohamad Ezral Baharudin , Nor A.M. , Mohd Zakimi Zakaria

Conventional modelling was once favored for process modelling for its straightforward nature and simplicity. However, conventional modelling is incapable of modelling complex processes such as fused deposition modelling (FDM). This study aims to model an accurate FDM process using an artificial neural network (ANN) to predict material consumption and tensile strength based on layer height, infill density, printing temperature and printing speed. The design of the experiment (DOE) was constructed using face-centered central composite design (FCCCD) yielding a total of 78 specimens. The material consumption was measured by weighting the specimen using a densimeter while the tensile strength of the specimen was tested using a universal testing machine (UTM). Best ANN structures were first identified in a trained network before being modelled for comparison. Models were compared using the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and highest coefficient of determination (R2). The best predictive model structure for material consumption is 4-19-14-1 with the lowest MSE of 0.00096 while the best predictive model structure for tensile strength is 4-16-15-12-1 with the lowest MSE of 0.005274145.

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System identification of flexible structure using firefly algorithm

2021-05-03 , Mohd Sazli Saad , Noor Fadhilah Mat Ros , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria , Azuwir Mohd Nor

This paper originally concentrates on application of firefly algorithm on modeling of flexible beam. An attempt of obtaining a linear parametric model is accomplished by acquiring the input-output data from the vibration of beam and followed by the selection the auto regressive with exogenous inputs (ARX) model structure as a benchmark for mathematical model of beam. The parameters of the model structure are identified using firefly algorithm. A few sets of parameter settings are tested to find the most optimal value. The best parameter setting estimation is selected based on performance of model. The model is compared with the model from conventional estimation approach and validated using validity test. It is found that firefly algorithm produces the best ARX model with the lowest MSE compared to the least square algorithm.

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Modelling and evolutionary computation optimization on FDM process for flexural strength using integrated approach RSM and PSO

2021-02-01 , Mohd Sazli Saad , Azuwir Mohd Nor , Mohd Zakimi Zakaria , Mohamad Ezral Baharudin , Wan Sallha Yusoff

Fused deposition modelling (FDM) is a modern rapid prototyping (RP) technique due to its potential to replicate a concept modelling, prototypes tooling and usable parts of complex structures within a short period of time. However, proper parameter selection is crucial to produce good quality products with reasonable mechanical properties, such as mechanical strength. In this study, four important process parameters, such as layer thickness, printing speed, print temperature and outer shell speed, are considered. These parameters are studied to observe their relationship towards the flexural strength of the polylactic acid (PLA) printed parts. The experimental design is conducted based on the central composite design in response surface methodology (RSM). Statistical analysis is performed using analysis of variance (ANOVA), in which the correlation between input parameters and output response is analysed. Next, the evolutionary algorithm optimisation approach, i.e., particle swarm optimisation (PSO), is applied to optimise the process parameters based on the regression model generated from the ANOVA. Results obtained from the PSO method are experimentally validated and compared with those of the traditional method (i.e., RSM). The flexural strength from experimental validation obtained using PSO exhibits an improvement of approximately 3.8%. The optimum parameters for layer thickness (A), print speed (B), print temperature (C) and outer shell speed (D) of approximately 0.38 mm, 46.58 mm/s, 185.45 °C and 29.59 mm/s result in flexural strength of 96.62 MPa.

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ANN-Based Predictive Modelling for Fused Deposition Modelling: Material Consumption, Tensile Strength & Dimensional Accuracy

2023-01-01 , Irazman H.N.H. , Mohd Sazli Saad , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria , Nor A.M. , Rahim Y.A.

Conventional modelling approaches fall short of accurately capturing the complexities of Fused Deposition Modelling (FDM). This research proposes an Artificial Neural Network (ANN) model to predict the FDM process's material consumption, tensile strength, and dimensional accuracy. Inputs such as layer height, infill density, printing temperature, and printing speed are considered. A Face-Centered Central Composite Design (FCCCD) with 78 specimens is employed to design experiments (DOE). Material consumption is measured using a densimeter, while tensile strength is determined using a Universal Testing Machine (UTM). The performance of the ANN models is evaluated based on metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The optimal ANN structure for material consumption prediction is found to be 4-19-14-1, achieving a low MSE of 0.00096. For tensile strength prediction, the best ANN structure is determined as 4-16-15-12-1 with an MSE of 0.005274145. Furthermore, dimensional accuracy is successfully captured using a 4-12-12-11-1 network configuration, which attains the lowest overall MSE of 0.002898. The proposed ANN model provides accurate predictions for material consumption, tensile strength, and dimensional accuracy in the FDM process. This study contributes to the optimization and understanding of FDM manufacturing processes through the utilization of optimized network architectures. The findings demonstrate the efficacy of the ANN model in improving FDM process control and performance.

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ARx modeling of flexible beam system using bat algorithm

2021-05-03 , Mohd Sazli Saad , Noor Fadhilah Mat Ros , Mohd Zakimi Zakaria , Mohamad Ezral Baharudin , Azuwir Mohd Nor

This paper describes the development of dynamic model of flexible beam system using system identification method based on nature inspired algorithm i.e. bat algorithm. At first, input-output data from the experimental rig of flexible beam were collected such that input signal is taken from piezo actuator and output signal from the laser displacement sensor. Then, linear parametric model structure is accomplished using auto regressive with exogenous inputs (ARX). The optimal parameters of the ARX model are identified using bat algorithm. The best parameter setting estimation is selected based on the best fit criterion i.e mean square error (MSE). The identified model is compared with the model from conventional estimation approach. Simulation results show that bat algorithm can outperform the least square algorithm in parametric modelling of the flexible beam.

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Optimization of FDM process parameters to minimize surface roughness with integrated artificial neural network model and symbiotic organism search

2022-01-01 , Mohd Sazli Saad , Azuwir Mohd Nor , Irfan Abd Rahim , Syahruddin M.A. , Mat Darus I.Z.

Fused deposition modeling (FDM) has shown to be a highly beneficial process for product development. However, one of the great challenges in using FDM is maintaining the surface quality of the produced part. Poor texture quality can be regarded as a defect. It is not part of the geometric prototype but results from the fabrication process. Poor input parameters typically cause these defects by the user. This paper presents the integration between an artificial neural network (ANN) and symbiotic organism search, known as ANN–SOS, to model and minimize the surface roughness (Ra) of the FDM process. The FDM input parameters considered were layer height, print speed, print temperature, and outer shell speed. The experimental data were collected using the central composite design response surface method. Then, the surface roughness model was established using an ANN. After validating the model's accuracy, it was combined with symbiotic organism search (SOS) to determine the optimal parameter settings for the minimum surface roughness value. The results illustrate that ANN–SOS with a 4-8-8-1 network structure would be the best model for surface roughness prediction. It was observed that decreasing the layer thickness, printing speed, print temperature, and outer shell speed of the FDM input parameters for ANN–SOS resulted in minimum surface roughness of approximately 2.011 µm, which was 12.36% better than the RSM method.

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