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
    Optimizing Surface Roughness of PLA Printed Parts using Particle Swarm Optimization (PSO)
    ( 2023)
    Hani Nasuha Hadi Irazman
    ;
    Mohd Sazli Saad
    ;
    Mohamad Ezral Baharudin
    ;
    Mohd Zakimi Zakaria
    ;
    Azuwir Mohd Nor
    ;
    Azizan As'arry
    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 %.
      1  15
  • Publication
    Improved dung beetle optimization algorithm and finite element analysis for spindle optimization
    (Institute of Advanced Engineering and Science (IAES), 2024)
    Ma Haohao
    ;
    Azizan As’arry
    ;
    Wu Xuping
    ;
    Mohd Idris Shah Ismail
    ;
    Hafiz Rashidi Ramli
    ;
    Mohd Sazli Saad
    ;
    Aidin Delgoshaei
    This research introduces an integrated optimization methodology for spindle design, combining the improved dung beetle optimization (IDBO) algorithm with finite element analysis (FEA). The IDBO algorithm, enhanced in population initialization and convergence factors, minimizes total deformation and mass, addressing a multi-objective optimization model. The obtained optimal parameters guide the construction of a finite element model, considering additional factors like stiffness and maximum stress. The ensuing FEA produces a foundation for constructing a response surface, further optimized to refine the initial design. Through the combination of the IDBO algorithm and FEA method, the mass of the spindle is reduced from 46.582 kg obtained by the IDBO algorithm solution to 28.479 kg, a total reduction of 38.86%, while meeting design requirements such as maximum total deformation. Modal analysis up to the sixth order validates the design correctness reveals dynamic spindle behavior and guarantees the design requirements. The study demonstrates the reliability and effectiveness of the proposed IDBO algorithm in conjunction with FEA, providing a versatile framework for engineering optimization.