Mohamad Ezral Baharudin
Thumbnail Image
Preferred name
Mohamad Ezral Baharudin
Official Name
Mohamad Ezral, Baharudin
Alternative Name
Baharudin, M. E.
Baharudin, Mohamad Ezral
Main Affiliation
Scopus Author ID
56016804900
Researcher ID
CCO-4698-2022

Research Output

Filters

9
9
Reset filters

Settings
Now showing 1 - 9 of 9
Thumbnail Image
Publication

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.

View
Thumbnail Image
Publication

Prediction of maximum spreading time of water droplet during impact onto hot surface beyond the Leidenfrost temperature

2021-12-01 , Suhaimi Illias , Suhaila Hussain , Rahim Y.A. , Muhammad Asri Idris , Mohamad Ezral Baharudin , Ismail K.A. , Ani M.H.

When a water droplet impacts on a heated surface in the film boiling regime, it will spread, recede, and finally bounce off from the heated surface. These unique liquid-solid interactions only occur at high surface temperatures. Our main objective in this research is to measure the maximum spreading and residence time of the droplet and the findings were compared to theory. We focused our study in the film boiling regime. Brass material was selected as the test surface and was polished until it became a mirror polished surface. The temperature range for this experimental work was between 100 °C up to 420 °C. Degassed and distilled water was used as the test liquid. The high speed video camera recorded the images at the rate of 10,000 frames per second (fps). As a result, it was found that the experimental value of maximum spreading and residence time agreed closely with the theoretical calculation. A new empirical formula that can be used to predict the maximum spreading time in the film boiling regime is also proposed.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
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 %.

View
Thumbnail Image
Publication

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.

View
Thumbnail Image
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.

View
Thumbnail Image
Publication

Critical heat flux and Leidenfrost temperature on Electrical Discharge Machining (EDM) - constructed hemispherical surface

2021-10-01 , Suhaimi Illias , Suhaila Hussain , Rosman N.A. , Mohamad Ezral Baharudin , Shaiful A.I.M. , Ismail K.A. , Ani M.H.

This paper reports a Leidenfrost temperature on hemispherical surface constructed by Electrical discharge machining or known as EDM. We focus our study on the droplet evaporation lifetime to investigate and identify the Leidenfrost temperature. Three (3) different types of materials were selected i. e such as Brass (Br), Aluminum (Al) and Copper (Cu). Meanwhile, ethanol liquid has been chosen as the test liquid. Ethanol liquid was elected due to its low boiling point of approximately 78 °C. The droplet impact velocity and droplet diameter was approximately 1.129 m/s and 3.476 mm, respectively. As a result, we finally succeeded in determining the Leidenfrost temperature for all materials mentioned above. On top of that, all the Leidenfrost temperature results, TL were close to the superheat limit temperature of ethanol liquid, TSL which is about 197.8 °C.

View