Department of Metallurgical and Materials Engineering

Permanent URI for this community

https://ir.unilag.edu.ng/handle/123456789/3477

Browse

Browsing Department of Metallurgical and Materials Engineering by Author "Agbeleye, A.A."

Now showing 1 - 3 of 3
  • Item
    Open Access
    Effect of Artificial Aging on Plane Anisotropy of 6063 Aluminium Alloy
    (International Scholarly Research Network ISRN Metallurgy, 2012-01-11) Adeosun, S.O.; Sekunowo, O.I.; Bodude, M.A.; Agbeleye, A.A.; Balogun, S.A.; Onovo, H.O.
    Most aluminum profiles’ production by deep-drawing and extrusion processes require certain degree of structural homogeneity because of the segregated second-phase particles in the as-cast structure. Rolled texture and directionality in properties often give rise to excessive earring, breakout, and tears. This study investigates the effect of heat treatment (artificial aging) on the anisotropic behavior of AA6063 alloy between rolling direction (0◦) through 90◦ directions. The results show significant reduction in property variability in the aged samples along the rolling direction 0◦, and 90◦ directions compared with the as-cast samples. This gave rise to improved % elongation, impact toughness, and substantial reduction (33.3%) in hardness. These results are capable of achieving huge savings in die conditioning and replacement with improved quality and sale of deep-drawn AA6063 alloy profiles for sustained profitability
  • Item
    Open Access
    MECHANICAL AND WEAR CHARACTERISTICS OF ALUMINIUM BRASS
    (Unilag Journal of Medicine, Science and Technology (UJMST), 2017) Esezobor, D.E.; Agbeleye, A.A.; Onovo, H.O.; Bodude, M.A.
    In this paper, the influence of the processing parameters on the wear and mechanical properties of 5 – 12 % aluminum red brass (Al-brass) was studied. The wear characteristics of developed Al brass in dry sliding conditions were exposed through a series of pin-on-disc sliding wear tests. Three load levels of 2, 7 and 12N, sliding speeds of 125 and 250 rpm and two sliding distances of 392.7 and 785.4m were investigated. The mechanical properties of the Al brass were determined using standard techniques. The results showed an increase in tensile strength from 225 MPa at 5 % aluminum addition to a maximum of 248 MPa at 10% Al and then a decline to 240 MPa at 12 % Al. The peak stress value increases as the weight percentage composition of Al increases until at 11%Al when it reduces. The impact energy and the hardness values of the as-cast Al brass rose from 54.2 Joules and 81HRC to 122 Joules and 92.4 HRC respectively at 12 % aluminum addition. At lower load of 2N, the addition of 5 % of Al brought a drastic improvement (65 %) to the wear resistance at 125 rpm and 250rpm, but the improvement became consistent thereafter. The same trends occurred at load of 7 N, but with lower degree of improvement (approximately 40 %). In contrast, under higher load of 12 N, the addition of Al brought slight and consistent improvement (10 -15 %) to the wear resistance.
  • Item
    Open Access
    Prediction of the abrasive wear behaviour of heat-treated aluminium-clay composites using an artificial neural network
    (2018) Agbeleye, A.A.; Esezobor, D.E.; Agunsoye, J.O.; Balogun, S.A.; Sosimi, A.A.
    This work employs the T6 heat treatment process to aluminium-clay (Al-Clay) composite consisting of 15 wt% clay. The samples were solutionized at 500°C, 550°C and 600°C, and were quenched in air, oil and water. Selected samples of the heat-treated composite were subjected to wear tests using Denison T62 HS pin-on-disc wear-testing machine in accordance with ASTM: G99-05 standard. The effects of two different loads (4 and 10 N) and three sliding speeds (200, 500 and 1000 rpm) under dry sliding conditions were investigated. The potential of using back-propagation neural network with 4-10-1 architecture was explored to predict the wear rate of the heat-treated composites. The results show that the performance of Levenberg–Marquardt training algorithm is superior to all other algorithms used. The well-trained ANN system satisfactorily predicted the experimental results and can be handy for an optimum design and also an alternative technique to evaluate wear rate.