Browsing by Author "Oladosu K. O"

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    Computer Aided Design and Drafting of Helical Gears
    (2012) Akinnuli B. O; Ogedengbe T. I; Oladosu K. O
    An interactive user friendly low cost software called “CADDgear” was developed in this study to facilitate the design and drafting of helical and spur gears thereby generating reliable data for use in manufacturing process. The software was developed, using JAVA programming language, as a tool for determining the design parameters and producing accurate and efficient 3D (three dimensional) and 2D (two dimensional) detail working drawings of helical gears. The study considered the existing approaches in use for the design of helical gears and then established a design analysis procedure for helical gear design. The established procedure was implemented through the developed software so that a substantial saving in term of time and cost of production of the design is obtained. The outcome of this research would enhance the designer’s productivity by reducing the time required to synthesis, analyze and document helical gear design. This would permit a thorough analysis of a large number of design alternatives. Results generated by the software shows very good agreement with that obtained through manual calculation using the established procedure. It was observed that the developed software successfully increase productivity over manual gear design and drafting by approximately thirty-four times in term of the time required for the design
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    Effects of Cold Extrusion on the Mechanical Properties of Scrapped Copper Coil
    (Sumy State University, Ukraine, 2021-12-10) Olawore A. S; Oladosu K. O; Sadiq T. O; Ahmed M; Adesope W. A
    The recycling of copper coil into finished products via sand casting with subsequent cold extrusion was investigated. This paper examined the effects of cold extrusion on the mechanical properties of the scrapped copper coil using a locally manufactured extruder with a conventional face die. The mechanical properties tested on the extrudates are limited to hardness, tensile, and compressive strength. The results reveal that the hardness of extruded copper of 11.10 mm and 11.45 mm improved significantly by 39 % and 41 %, respectively, compared with respective non-extruded copper. The compressive and tensile strength increases by 42 % and 22 %, respectively, for 11.10 mm extruded copper compared with the corresponding non-extruded copper. Also, the elongation of the extruded copper of 11.10 mm and 11.45 mm increases by 33 % and 34 %, respectively. It was deduced that the extruded copper is more ductile than the non-extruded copper. The micrograph reveals that grains in non-extruded copper are relatively coarse and nonuniform with voids, but fine and relatively uniform grains are obtained in extruded copper. The grains are refined during cold extrusion, and voids and islocations are reduced significantly
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    OPTIMIZATION AND MODELLING OF BIO-OIL YIELD FROM THE PYROLYSIS OF JATROPHA CURCAS SEED USING OPTIMAL DESIGN AND ARTIFICIAL NEURAL NETWORKS
    (LAUTECH Journal of Engineering and Technology, 2023) Oladosu K. O; Amoloye T. O; Mustapha K; Oderinde J. O; Babalola S
    Better quality and quantity of pyrolysis products from biomass can be obtained by regulating the input parameters of the pyrolysis process. Pyrolysis of Jatropha Curcas seed mixed with alumina catalyst was carried out in a fixed bed reactor to study the effects of temperature, time, and particle size on bio char and bio-oil yield. The bio-oil yields were optimized using the Optimal Design (OD) under the Combined Methodology of the Design-Expert Software (12.0). The input and output parameters were modeled and validated using Artificial Neural Networks (ANN) based on 40 experimental data generated by the OD. The optimum bio-oil yield of 15.6 wt. % was obtained at 650 °C, 30 min, and 1 mm particle size. The Correlation Coefficient (R2) of the model for the bio-char and bio-oil yield under the OD were 0.998 and 0.996, respectively. The optimized ANN architecture employed the in-built Levenberg-Marquardt training algorithm in MATLAB software. Random division of the data into training, validation and testing sets followed 70:15:15 percentage proportions with 15 hidden layers. This resulted in the minimum Mean Square Error (MSE) of 2.15e-05 and (R2) of 0.96394 for the bio-oil yield. The FTIR spectra indicated that bio-oil contained phenols, esters, and acids compound while its Gas Chromatography analysis showed the presence of pyrrolidine, pyrimidine, and aldehydes. These properties signified the bioenergy and biochemical capabilities of the pyrolytic oil obtained. The prediction accuracy indicates that both the ANN and OD can be deployed for accurate prediction.