Mohd Zakimi Zakaria
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Mohd Zakimi Zakaria
Official Name
Mohd Zakimi , Zakaria
Alternative Name
Zakaria, Mohd Z.
Zakaria, M. Z.
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Scopus Author ID
36999011800
Researcher ID
D-7223-2015

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  • Publication
    Medium sized industrial motor solutions to mitigate the issue of high inrush starting current
    ( 2021-05-03)
    Azizan N.S.
    ;
    Azizan M.M.
    ;
    Muhammad Izuan Fahmi Romli
    ;
    Muhammad Zaid Aihsan
    ;
    Muzammil Jusoh
    ;
    Nashrul Fazli Mohd Nasir
    ;
    Mohd Zakimi Zakaria
    Starting of a medium or large induction motors generate such a large current during direct-on-line (DOL) starting process to the point that it can drop the voltage of power supply. The induction motor can be broken, its characteristics can be changed, and performances of the motor can be worsened. A significant higher starting current than the rated current can generate mechanical and thermal stress on the motor and the loads. High-voltage fluctuations, dips and sags can arise in electrical power systems associated with the motor. To overcome this problem, various starters were designed. There are several types of starters in which can be divided into conventional starters and power electronic drives. Conventional starters are such as direct-online, star-delta and autotransformer whereas the example for power electronics are matrix converter, frequency inverter and soft starter. In this paper, the design of autotransformer and soft starter are focused in order to compare inrush current during the start-up three phase medium sized industrial induction motor by using MATLAB/Simulink software. Both starters were targeted to resolve the problems inherent in the dynamic operation of induction motors, which included current and torque surges during the motor start up. The autotransformer gives the choice to consumer in selecting different tap values, in which is the advantage to consumer to vary their starting current and starting torque according to its application. On the other hand, the three-phase soft starter employs two anti-parallel connected switches in each phase. Thyristors act as the switches due to their higher power rating and high efficiency. Then, both methods will be compared to prove the best performance in mitigate high inrush current. The best of the two is likely to be the answer for mitigating the issue of high inrush starting current for a medium size motor at industries.
  • Publication
    Synthesizing and Optimization the Hydroxyapatite Based on Corbiculacea Seashells
    ( 2021-01-01)
    Mohd Riza Mohd Roslan
    ;
    Nashrul Fazli Mohd Nasir
    ;
    Nur Farahiyah Mohammad
    ;
    Cheng Ee Meng
    ;
    Nasrul Amri Mohd Amin
    ;
    Abdul Khalid M.F.
    ;
    Mohd Zakimi Zakaria
    ;
    Muhammad Mokhzaini Azizan
    ;
    Muzammil Jusoh
    Hydroxyapatite (HA) is one of the main components in bone which functions to enhance its cell regeneration. Synthetically produced HA, based on seashell resources has higher biocompatibility, and in high demand especially in bone tissue engineering. However, the secondary phase of HA production are calcium oxide and carbonate, which impedes its performance. HA is synthesized via wet chemical precipitation and optimization were done to obtain nearly pure HA by manipulating the pH value and sintering temperature. It is expected that the combination of these parameters will optimize the amount of secondary phase hence attained nearly stoichiometric or pure HA. HA powders were analyzed through Fourier Transform Infrared Spectroscopy (FTIR) and Energy Dispersive X-ray Spectrometry (EDX).
      1
  • Publication
    Research Objective in Assembly Line Balancing Problem: A Short Review
    ( 2021-01-01)
    Nurhanani Abu Bakar
    ;
    Mohd Zakimi Zakaria
    ;
    Mohammad Fadzli Ramli
    ;
    Nashrul Fazli Mohd Nasir
    ;
    Muhammad Mokhzaini Azizan
    ;
    Muzammil Jusoh
    Presently, the arising of industrial revolution has encouraged the advance of technology especially in manufacturing engineering industry. Assembly line balancing (ALB) plays very importance role in manufacturing due to improve the process of efficiency and increase the production rate. In this case, authors have a tendency to study and explore in detail about assembly line balancing problem (ALBP). This paper aims to review the research objectives on ALBP based on 60 articles that try to solve this problem using various approach. In general, this paper performs a survey of ALBP research from 2017 to 2018 in order to see the trend of current study. The publication that listed in this survey have create some division section of ALBP research that describe in short the classification of ALB and the objectives of research. The literature review in research objective may highlight the significant objectives in ALB and provide guidance for future work to look into the research gap.
      1
  • 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.
      4
  • 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.
      4  2
  • 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.
      5  20
  • Publication
    Addressing labour ergonomics through automation in oil palm plantation activities a necessity for sustainable agriculture
    ( 2024)
    Azuwir Mohd Nor
    ;
    Mohd Sazli Saad
    ;
    Mohamad Ezral Baharudin
    ;
    Mohd Zakimi Zakaria
    A key element of the potential of robotics is understanding how effective automation can improve labour‐intensive jobs while also considering worker ergonomics. These sectors often depend on manual labour, which exposes employees to considerable ergonomic stress, especially musculoskeletal disorders (MSDs) that can result from repetitive and physically demanding activities like harvesting, pruning, and lifting heavy items. By coordinating automation tools such as harvesters, unloaders, and driverless carts with the various manual tasks that workers perform, we can significantly lower safety risks. The main objective of introducing automation is to reduce the physical strain on workers, which not only aims to alleviate MSD‐related health problems but also helps to lessen worker fatigue. Effectively integrating artificial intelligence (AI) and big data analytics will improve workforce efficiency, making the Brightfield industry stronger. Transitioning from manual tasks to automated solutions is just the initial step toward enhancing production in this field. By tackling these ergonomic issues through automation, this paper highlights the dual advantages of promoting worker health and increasing productivity in the industry.
      1  35
  • Publication
    Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)
    ( 2021-11-25)
    Lubis A.J.
    ;
    Nashrul Fazli Mohd Nasir
    ;
    Zulkarnay Zakaria
    ;
    Muzammil Jusoh
    ;
    Azizan M.M.
    ;
    Mohd Zakimi Zakaria
    ;
    Mohd Nor Fakhzan Mohd Kazim
    ;
    Rahman S.
    Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution's internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose.
      1  15
  • Publication
    Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
    (Penerbit UTM Press, 2015)
    Mohd Zakimi Zakaria
    ;
    Hishamuddin Jamaluddin
    ;
    Robiah Ahmad
    ;
    Azmi Harun
    ;
    Radhwan Hussin
    ;
    Ahmad Nabil Mohd Khalil
    ;
    Muhammad Khairy Md Naim
    ;
    Ahmad Faizal Annuar
    This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.
      3  11
  • 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.
      2  13