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Faculty of Engineering and Built Environment

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Browsing Faculty of Engineering and Built Environment by Subject "0912 Materials Engineering"

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    Advances in sintering of titanium aluminide : a review
    (Springer Science and Business Media LLC, 2022) Mphahlele, Mahlatse R.; Olubambi, Peter Apata; Olevsky, Eugene A.
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    Artificial intelligence-based modeling of compressive strength of slurry infiltrated fiber concrete
    (Emerald, 2024-12) Oyebisi, Solomon; Shammas, Mahaad Issa; Sani, Reuben; Oyewola, Miracle Olanrewaju; Olutoge, Festus
    The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach. Design/methodology/approach This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics. Findings The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R2 and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact. Originality/value This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.
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    Nanoindentation mechanical properties on spark plasma sintered 48Ti-48Al-2Cr-2Nb alloy
    (Elsevier BV, 2021) Mphahlele, Mahlatse R.; Olevsky, Eugene; Tshephe, Thato; Olubambi, Peter A.; Jen, Tien-Chien; Machaka, Ronald; Olubambi, Peter
    This study aims to investigate the microstructure, plastic (H) properties, elastic (E) properties, reduced elastic (Er) properties the strain-to-break parameter (H/Er), and the resistance to plastic deformation parameter (H3/Er2) of the Ti-48Al-2Cr-2Nb alloy by use of scanning electron microscopy, nanoindentation and micro-indentation techniques. The results show that the sintering parameters had significant effect on the resulting microstructure. Desirable mechanical properties were obtained with the sample sintered at temperature of 1200 °C, pressure of 50 MPa, holding time of 7.5 min and a heating rate of 50 °C/min which had a near lamellar structure, resulting from the grain boundary pinning effect of the fine equiaxed gamma grains and the impartation of ductility due to the coarsened lamellar colonies. The nano-hardness and elastic modulus were observed to be about 4GPa and 31GPa for the near lamellar microstructure, respectively, with the microhardness of about 4.4GPa. While the duplex and the near gamma microstructures possessed the least nano-hardness (3.65–3.78GPa) and elastic modulus (3.6–29.5GPa) with the exception of sample sintered at temperature of 1150 °C, pressure of 50 MPa, holding time of 7.5 min and a heating rate of 100 °C/min., with nano-hardness and elastic modulus of 4.05GPa and 31.25GPa, respectively, however it had the lowest micro-hardness of 2.7GPa. Furthermore, the ratios H/Er and H3/Er2 values were observed to be greater for the same sample suggesting good wear resistance of the alloy.