Azuwir Mohd Nor
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Preferred name
Azuwir Mohd Nor
Official Name
Azuwir, Mohd Nor
Alternative Name
Mohd Nor, Azuwir
Nor, A. M.
Nor, Azuwir Mohd
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Scopus Author ID
57201741934
Researcher ID
GOI-2498-2022

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  • 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.
      12  32
  • 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.
      3  32
  • 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.
      2  33
  • 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.
      10  29
  • Publication
    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.
      12  37