Shayfull Zamree Abd. Rahim
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Preferred name
Shayfull Zamree Abd. Rahim
Official Name
Shayfull Zamree, Abd. Rahim
Alternative Name
Shayfull, Zamree Abd Rahim
Abd Rahim, Shayfull Zamree Abd
Shayfull, Zamree
Abd Rahim, S. Z.
Abd Rahim, Shayfull Zamree Bin
Bin Abd Rahim, S. Zamree
Abd Rahim, S.
zamree bin abd Rahim, Shayfull
Rahim, Shayfull Zamree Abd
Zamree Abd Rahim, Shayfull
Zambree, Shayfull
Rahim, Shayfull Z.B.Abd
Zamree, A. R.Shayfull
Shayfull, Z.
Main Affiliation
Scopus Author ID
54941291700
Researcher ID
I-2840-2019

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  • Publication
    Optimization of Injection Moulding Process via Design of Experiment (DOE) Method based on Rice Husk (RH) Reinforced Low Density Polyethylene (LDPE) Composite Properties
    ( 2022-01-01)
    Haliza Jaya
    ;
    Nik Noriman Zulkepli
    ;
    Mohd Firdaus Omar
    ;
    Shayfull Zamree Abd. Rahim
    ;
    Nabiałek M.
    ;
    Jez K.
    ;
    Mohd. Mustafa Al Bakri Abdullah
    Optimal parameters setting of injection moulding (IM) machine critically effects productivity, quality, and cost production of end products in manufacturing industries. Previously, trial and error method were the most common method for the production engineers to meet the optimal process injection moulding parameter setting. Inappropriate injection moulding machine parameter settings can lead to poor production and quality of a product. Therefore, this study was purposefully carried out to overcome those uncertainty. This paper presents a statistical technique on the optimization of injection moulding process parameters through central composite design (CCD). In this study, an understanding of the injection moulding process and consequently its optimization is carried out by CCD based on three parameters (melt temperature, packing pressure, and cooling time) which influence the shrinkage and tensile strength of rice husk (RH) reinforced low density polyethylene (LDPE) composites. Statistical results and analysis are used to provide better interpretation of the experiment. The models are form from analysis of variance (ANOVA) method and the model passed the tests for normality and independence assumptions.
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