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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Determining the optimal mix of institutional geopolitical power and ASEAN corporate governance on the firm value of Malaysia’s Multinational Corporations (MNCs)

2018 , Wan Sallha Yusoff , Handayani Wuri , Mohd Sazli Saad , Md. Salleh Mohd. Fairuz

The purpose of this paper is to examine the relationship between institutional geopolitics, ASEAN corporate governance quality and the firm value of Malaysia’s multinational corporation (MNC). We used the data of MNCs in Malaysia that were active from 2009 to 2013 as an evidence of MNCs from emerging market economies. Descriptive analysis, factor analysis and panel data analysis have been utilized to test the equation model. We also propose optimization analysis by using differential evolution method to capture the optimal mix of institutional geopolitics and ASEAN_CG on the firm value of MNC. Results reveal that the geopolitics of G7(Canada, France, German, Italy, Japan, Europe, and the United States), BRICS (Brazil, Russia, India, China, and South Africa), and ASEAN (Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Myanmar, Philippines, Singapore, Thailand, Vietnam, and Malaysia) are highly correlated with the firm value of Malaysia’s MNC. The power of institutional geopolitics, namely, military, material, and social power, influences firm value negatively and ASEAN_CG moderate the negative influence of institutional geopolitics on the firm value of MNC. Thus, it is importance for corporate management to understand the geopolitical changes of host countries’ and increase the compliance of ASEAN_CG in formulating their market value and segmentation strategies.

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Review of Control Strategies for Improving the Photovoltaic Electrical Efficiency by Hybrid Active Cooling

2024-06-01 , Edaris Z.L. , Mohd Sazli Saad , Mohammad Faridun Naim Tajuddin , Ali M.H. , Mohamad Shukor Abdul Rahim

Photovoltaic (PV) cooling systems are used widely in order to increase the PV efficiency. Most review paper was published for the role, design and cooling techniques of PV applications, there is a lack of collected and organised information regarding the latest and the newest updates on control strategies for PV cooling control systems. Hence, this paper presents a comprehensive review of PV cooling control strategies discussing the latest research works during the years from 2010 to 2022. PV/T hybrid cooling types are highlighted, followed by the main focus of this paper an extensive review of the control schemes for diverse types of PV cooling systems that have been carried out. This paper summarises most of the related work and also pays a special focus on research trends regarding the control of PV cooling systems that have been previously published in the literature. This review paper will be helpful to new researchers when identifying research directions for this particular area of interest.

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Prediction of the material consumption of PLA plus fused deposition models using artificial neural network technique

2024-04-22 , Nasuha H. , Mohd Sazli Saad , Mohamad Ezral Baharudin , Azuwir Mohd Nor , Mohd Zakimi Zakaria

Fused Deposition Modelling (FDM) is a complex additive manufacturing (AM) process involving multiple process parameters incapable of being modelled with conventional methods such as regression and mathematical modelling. The goal of the study is to develop an Artificial Neural Network (ANN) model that can accurately predict the material consumption of FDM printed parts considering the effect of process parameters such as layer height, infill density, printing temperature, and printing speed to create an ideal model that can optimize the use of resources and reduce material. The experiment was designed using face centered central composite design (FCCCD) yielding 78 specimens that were weighed using a densimeter to identify material consumption. Then, three networks with a different number of hidden layers and neurons were trained to identify the best-performing ANN structure with 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 fittest models were modelled and compared to identify the best-performing structure. Results indicated that the ANN model with double hidden layers with 19 and 14 neurons each showed the most precise prediction in modelling material consumption with the lowest MSE of 0.00096.

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Risk evaluation on D power press operation report at D manufacturing company in Malaysia

2021-05-03 , Mohd Fathullah Ghazli@Ghazali , Mohd Sazli Saad , Osman M.S. , Ling C.J. , Li F.H. , Yee L.S. , Lee M.J. , Shing S.C.

This paper reports a case study of accidents or incident analysis in the power press machine at a press machine area in a manufacturing company. Hazard identification, cause and effect, risk level and solutions was assessed. There are total of seven hazards identified in the power press machine. These seven hazards consist of six medium level of risk and one low level of risk respectively. These listed hazards in the workplace will directly affect the worker in the view of safety and health. However, there are some solutions regarding these hazards had been proposed. By carry out some discussion, certain solution is suggested to solve the hazard in the workplace. The suggested solution shown in the HIRARC analysis can be said as simple solution which the company can conduct it successfully. It can help to reduce or solve the hazards that will protect their workers well. This will also increase their worker's productivity and comfortability in work. Pareto analysis had shown clearly with the higher risk hazards consist in the workplace that can let the company to solve the hazards with priority according to the analysis shown. Pareto analysis will help to solve the problem effectively. All these solutions suggested will only take about 2 months to solve all the hazards.

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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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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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A comparative study of numerical modelling and analysis for large articulated pendulums

2025-05 , Siti Fatimah Azzahra Ahmad Noh , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria , Mohd Sazli Saad , Azuwir Mohd Nor

In this article, we present a large system of multiple pendulums, also articulated pendulums, with twenty pendulums as a multibody model. The main objective of the study is to compare the computational time efficiency of two multibody formulations: the augmented Lagrangian and the recursive method for each articulated system. The equations of motion were derived for each formulation and the fourth- and fifth-order Runge-Kutta methods were utilised to solve for the equations by representing the kinematics and dynamics of the systems numerically. The computational times that corresponded to the manipulated step size and tolerance were compared for both formulations. The results showed that the augmented Lagrangian formulation had a significant divergence towards the negative y-axis at tolerance 0.1s for all modified step sizes. The animations also demonstrated elongation for specific pendulums based on the step size selection at a tolerance 0.1s. The recursive method, on the other hand, produced the best-fit plots and stable results for all xy-position and velocity-time plots for each adjusted step size and tolerance. Therefore, the recursive method is concluded to be more efficient than the augmented Lagrangian formulation in solving large open-loop multibody systems.

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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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Elektronik asas : untuk pelajar mekanikal

2015 , Amarul Talip , Mohd Sazli Saad , Mohd Fathullah Ghazli@Ghazali , Nur Ismalina Haris

Buku ini mengandungi lapan (8) bab kesemuanya di mana ia telah disusun dan mencakupi bab-bab asas yang penting untuk membentuk satu ilmu asas Teknologi Elektrik yang lengkap untuk para pelajar Kejuruteraan Mekanikal. Antara bab-bab tersebut ialah, Asas Kejuruteraan Elektrik, Litar Arus Terus, Litar Arus Ulang-alik, Sistem Tiga Fasa, Elektromagnetik, Pengubah, Mesin Arus Terus dan Mesin Pearuh Tiga Fasa. Di samping itu, buku ini juga diharapkan dapat menjadi rujukan para pelajar dari politeknik khasnya dan institusi pengajian tinggi amnya kerana bilangan buku-buku rujukan yang terdapat dalam Bahasa Melayu adalah terhad. Dalam usaha untuk menambah bilangan buku-buku rujukan yang ditulis dalam Bahasa Melayu, penghasilan buku ini diharapkan dapat membantu pelajar Kejuruteraan khususnya Kejuruteraan Mekanikal untuk lebih memahami dan menguasai Asas Teknologi Elektrik.

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