Multi objective optimization of novel Al-Si-Mg nanocomposites: A Taguchi-ANN-NSGA-II Approach

Kelsy, Braide T.; Nwobi-Okoye, Chidozie Chukwuemeka; Ezechukwu, Vincent Chukwuemeka; Uche, Remy

doi:10.1016/j.jer.2023.10.008

Cited by 1 publication

(2 citation statements)

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“…Based on the ANOVA and Table 5, it was inferred that factor C (normal load), had a significant influence on the WR, with a substantial influence of 92.33%. The high percentual influence of normal load on wear rate is possibly due to the higher applied loads compared to other studies [11,15,[57][58][59][60]; however, it corresponds to the findings presented in [26,34,53]. The rest of the influence is distributed among other factors as follows: the amount of Al 2 O 3 nanoparticle influence was 1.71%, the micro-reinforcement type influence was 1.42%, and the influence of the interactions between the factors A and C was 3.79%.…”

Section: Taguchi Grey Analysissupporting

confidence: 83%

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Tribological Behaviour of Hypereutectic Al-Si Composites: A Multi-Response Optimisation Approach with ANN and Taguchi Grey Method

Miladinović,

Gajević,

Savić

et al. 2024

Lubricants

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An optimisation model for small datasets was applied to thixocasted/compocasted composites and hybrid composites with hypereutectic Al-18Si base alloys. Composites were produced with the addition of Al2O3 (36 µm/25 nm) or SiC (40 µm) particles. Based on the design of experiment, tribological tests were performed on the tribometer with block-on-disc contact geometry for normal loads of 100 and 200 N, a sliding speed of 0.5 m/s, and a sliding distance of 1000 m. For the prediction of the tribological behaviour of composites, artificial neural networks (ANNs) were used. Three inputs were considered for ANN training: type of reinforcement (base alloy, Al2O3 and SiC), amount of Al2O3 nano-reinforcement (0 and 0.5 wt.%), and load (100 and 200 N). Various ANNs were applied, and the best ANN for wear rate (WR), with an overall regression coefficient of 0.99484, was a network with architecture 3-15-1 and a logsig (logarithmic sigmoid) transfer function. For coefficient of friction (CoF), the best ANN was the one with architecture 3-6-1 and a tansig (hyperbolic tangent sigmoid) transfer function and had an overall regression coefficient of 0.93096. To investigate the potential of ANN for the prediction of two outputs simultaneously, an ANN was trained, and the best results were from network 3-5-2 with a logsig transfer function and overall regression coefficient of 0.99776, but the predicted values for CoF in this case did not show good correlation with experimental results. After the selection of the best ANNs, the Taguchi grey multi-response optimisation of WR and CoF was performed for the same combination of factors as the ANNs. For optimal WR and CoF, the combination of factors was as follows: composite with 3 wt.% Al2O3 micro-reinforcement, 0.5 wt.% Al2O3 nano-reinforcement, and a load of 100 N. The results show that developed ANN, the Taguchi method, and the Taguchi grey method can, with high reliability, be used for the optimisation of wear rate and coefficient of friction of hypereutectic Al-Si composites. Microstructural investigations of worn surfaces were performed, and the wear mechanism for all tested materials was light abrasion and adhesion. The findings from this research can contribute to the future development of hypereutectic Al-Si composites.

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Section: Taguchi Grey Analysissupporting

confidence: 83%

“…ANOVA helps to validate the significance of factors in terms of their contribution. The final phase encompasses a confirmation test, which is conducted based on the estimated process parameter values [52][53][54].…”

Section: Taguchi Designmentioning

confidence: 99%