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2 2018

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Structural response of multilayered aluminum and steel specimens subjected to high strain rate loading conditions

Iqbal D., Tiwari V.

Journal of Theoretical and Applied Mechanics

2018

Vol. 56 nr 4

1139--1151

Series of experiments and a detailed computational analysis has been performed to investigate the high strain rate behaviour of homostacked Al 6063-T6 and IS 1570 alloys. Split Hopkinson pressure bar technique was utilized to study the effect of high rate loading on the stress strain relationship of single, double, tri and quad layered/stacked specimens. Three different specimen aspect ratios 1, 0.75 and 0.5 were also evaluated for different strain rates. A 2 mm thick pulse shaper was employed in achieving dynamic stress equilibrium, a near constant strain rate and a high rise time as per requirements. After analyzing the results from the experiments it was observed that single and halved specimens showed a close match in both the elastic and plastic regions for aluminium alloy as well as for steel. In the case of Al 6063-T6, a nearly bi-linear nature of the constitutive curve was observed for single and halved specimens, which transformed into near tri-linear nature for tri and quad stacked specimens. The dynamic numerical analysis showed a good agreement between the numerical and experimental results for a single and halved specimen in the case of Al alloy. For steel, a close correlation was observed for all the four cases.

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Farooq A., Sarwar M. I., Ashraf M. A., Iqbal D., Hussain A., Malik S.

Autex Research Journal

2018

Vol. 18, no. 4

429--433

Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.