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

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  • Publication
    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.
  • Publication
    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.
  • Publication
    Asas teknologi elektrik : untuk pelajar mekanikal
    ( 2015)
    Amarul Talip
    ;
    Mohd Sazli Saad
    ;
    Irfan Abdul Rahim
    ;
    Mohd Fathullah Ghazli@Ghazali
    ;
    Mohd Khairul Fadzly Abu Bakar
    ;
    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.
  • Publication
    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.
  • Publication
    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.
  • Publication
    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.
  • Publication
    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
    ;
    Nor A.M.
    ;
    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.
  • Publication
    Application of Differential Evolution (DE) Optimization Method in CNC Turning Process for Surface Roughness
    ( 2021-01-01)
    Azuwir Mohd Nor
    ;
    Mohammed Faozi Ahmed Alsakkaf
    ;
    Mohd Sazli Saad
    ;
    Mohd Zakimi Zakaria
    ;
    Mohamad Ezral Baharudin
    This paper presents the optimization of cutting speed, feed rate and depth of cut during CNC turning process of ASTM A36 Mild steel in order to minimize the surface roughness. The default parameters setting in some cases of machining process using conventional optimization technique does not guarantee the best surface roughness quality of the machined part. Therefore, in this study, a non-conventional method, Differential Evolution (DE) optimization, has been developed to address this problem. At first, a regression model using Response Surface Method (RSM) was developed using experimental data. The experiment was designed by using DOE method. Central composite design was applied in Design Expert software for building a second order (quadratic) model for minimum surface roughness. Then, DE algorithm was implemented using Matlab programming. The performances of both conventional and non-conventional techniques were compared through experimental validation tests. The results showed that optimal parameters setting values provided by DE obtained better results than RSM. Thus, in this study, DE can be considered as an efficient and effective technique to achieve a better surface roughness.
  • Publication
    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.
  • Publication
    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 %.