Yuzairi Abdul Rahim
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Preferred name
Yuzairi Abdul Rahim
Official Name
Yuzairi, Abdul Rahim
Alternative Name
Rahim, Y. A.
Rahim, Y. Abdul
Abdul Rahim, Y.
Main Affiliation
Scopus Author ID
57201860639
Researcher ID
EJY-7689-2022

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Experimental and finite element modeling of partial infill patterns for thermoplastic polymer extrusion 3D printed material using elasto-plastic method

2020-10-26 , Mohd Sabri Hussin , Sanusi Hamat , Syamir Alihan Showkat Ali , Muhammad Aiman Ahmad Fozi , Yuzairi Abdul Rahim , Mohd Syedi Imran Mohd Dawi , Darsin M.

Fused Deposition Modeling is known as one of the 3D printing technology where it used a thermoplastic filament to produce a prototype or a 3D part. FDM will print out the 3D part, layer by layer on the platform of the 3D printer from bottom to top using the extruded molten thermoplastic. However, there is no information about the volume enclosed by the boundary surface of the 3D part by commonly used model data format such as STL file, since the volume enclosed can be completely or partially filled. Therefore, the study and research have been carried out to investigate the strength of the 3D part affected by the design of the infill pattern where three methods being used which are design, experiment and simulation. The 3D parts were designed using CATIA V5 following the ASTM D638 for tensile test and ASTM D695 for compression test. The 3D design was then being printed using the Fused Deposition Modeling (FDM) technique for experimental purposes and to perform the quasi-static test. Furthermore, the 3D printed with infill pattern test data were then being imported to ABAQUS/Explicit software for non-linear finite element analysis using elasto-plastic approach. The best infill patterns that exhibit a better strength after the 100% fill part is the 30% fill Lines pattern. It can be concluded that the average percentage error of stress and strain values between experimental test and simulations in tensile and compression for all specimens is below than 10%.

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Experimental Performance of R134a/SiO2 in Refrigeration System for Domestic Use

2022-01-01 , Mohd Hisham Che Hussin , Sa'adah Ahmad @ Ahmad Sowi , Muhammad Adlin Syahar Mahadi , Asmawi Sanuddin , Ahmad Nabil Mohd Khalil , Yuzairi Abdul Rahim

Nanofluids are considered as a new invention of fluids having superior thermal physical properties to improve efficiency of the refrigeration system. Nanofluids are the colloidal suspensions of nanoparticles in base fluid. Nanoparticles having higher thermal conductivity compared to pure refrigerant such as R134a can be added to pure refrigerant to improve the performance of refrigeration system. This study focuses on producing nanolubricant (SiO2/POE) and implementing the nanolubricant into refrigeration system. The nanoparticles will be homogenized in refrigerant to produce nanoRefrigerant (R134a/SiO2) at the attached reservoir. The aim of the research is to study the thermal physical properties of nanolubricant and to find the relationship between nanoparticles’ volume fraction to the Coefficient of Performance (COP) of the refrigeration system. The investigations are focused on the effects of nanoparticles with 0.1, 0.3%, 0.7% and 0.9% volume fraction to the performance of the refrigeration system. The results show that the usage of nanolubricant creates higher thermal conductivity with slightly higher dynamic viscosity which eventually increase the performance of the refrigeration system by 8.62% in term of COP.

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A Review on the efficiency and accuracy of localization of moisture distributions sensing in agricultural silos

2019-12-03 , Almaleeh A.A. , Ammar Zakaria , Zakaria S.M.M.S. , Latifah Munirah Kamarudin , Mohd Hafiz Fazalul Rahiman , Sukor A.S.A. , Yuzairi Abdul Rahim , Abdul Hamid Adom

The moisture distribution in the silos depends upon various seeds parameters such as type and size of seeds, amount of storage, external weather, and storage period as well as structural and environmental factors. It is very difficult to predict moisture distribution in silos effectively while taking all the above aspects into consideration. This study aims to investigate the efficiency and accuracy of localization of moisture distributions sensing in agricultural silo. The work is mainly focussed on three major elements: Radio Frequency (RF), tomographic imaging and classification process using machine learning. In particular, RF-based signal and volume tomographic images are used to predict the moisture distribution. Furthermore, computational intelligence techniques such as artificial neural network (ANN) is applied to develop models based on previous data. The generalization of these models towards new set of data is discussed in making sure a successful application of a model. A detailed study of the relative performance of computational intelligence techniques has been carried out based on different statistical performance criteria.

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