Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Prediction

Prediction of Fatigue Life of Fiber Glass Reinforced Composite (FGRC) using Artificial Neural Network

Lateef; M.S. Abdu; N.S. Abdulrazaq; A.G. Mohammed

Engineering and Technology Journal, 2017, Volume 35, Issue 4, Pages 327-339

The present work studies the mechanical properties of composite materials, experimentally and analytically, that are fabricated by stacking 4-layers of fiberglass reinforced with polyester resin. This plies are tested under dynamic load (fatigue test) in fully reversible tension-compression (R=-1) to estimate the fatigue life of the composite where fatigue performance of fiberglass reinforced composed is an increasingly important consideration especially when designing wind turbine blades. In order to predict fatigue life (Number of cycles to failure), conventional analytical techniques are used in the present work. In addition, Artificial Neural Network (ANN) is a reliable and accurate technique that is used for predicting fatigue life. The used networks are; Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial Bases Function Neural Network (RBFNN). Based on the comparison of the results, it is found that the ANN techniques are better than conventional methods for prediction. The results shows that (RBNN2), where stress load and angle of orientation are input to the network and number of cycles to failure as output, is an efficient tool for prediction and optimization the fatigue life of fiberglass reinforced composite.

Prediction of Surface Roughness and Material Removal Rate for 7024 AL-Alloy in EDM Process

Abbas Fadhil Ibrahim; Mostafa Adel Abdullah; Safaa Kadhim Ghazi

Engineering and Technology Journal, 2016, Volume 34, Issue 15, Pages 2796-2804

This paper studies prediction the values of MRR and surface roughness in Electrical discharge operations. It is a operation in which the material removal rate is machined with elevation spark in the midst work piece and electrode sunken through dielectric solution.Through use Taguchi found that the accuracy of the measured and prediction values that have been is 93% and 99% for each of the MRR and surface roughness respectively. The effect of different Electrical discharge machining factors are (Gap, pulse off time and pulse on time) to predict the (material removal rate) and (roughness). Note that connected pole that was used is copper. From (ANOVA) found that the large parameter effect on MRR is pulse-on 65% and pulse-off 25% while large parameter effect for surface roughness is pulse-on 96% . The least influential parameter for metal removal rate is the gap and the least influential parameter for surface roughness is pulse-off and Gap.

Effect of Dual Reinforcement on Wear Resistance by Aluminum Compacts Reinforce by SiC, Al2O3

Mohammed Moanes Ezzaldean Ali; Hanan A. R. Akkar; A. K. M. AL-Shaikhli; Ali K. Shayyish; Muhsin J. Jweeg; Wisam Auday Hussain; Mohammed T. Hussein; Mohammad A. Al-Neami; Farah S. Al-Jabary; Jafar M. Hassan; Ali H. Tarrad; Mohammed N. Abdullah; Ahmed T. Mahdi; Eyad K. Sayhood; Husain M. Husain; Nidaa F. Hassan; Rehab F. Hassan; Akbas E. Ali; Assim H Yousif; Kassim K Abbas; Aqeel M Jary; Shakir A. Salih; Ali T. Jasim; Ammar A. Ali; Hosham Salim; JafarM. Daif; Ali H. Al Aboodi; Ammar S. Dawood; Sarmad A. Abbas; Salah Mahdi Saleh; Roshen T. Ahmed; Aseel B. Al-Zubaidi; Mohammed Y. Hassan; Majid A. Oleiwi; Shaimaa Mahmood Mahdy; Husain M. Husain; Mohammed J. Hamood; Shaima; a Tariq Sakin

Engineering and Technology Journal, 2009, Volume 27, Issue 13, Pages 423-429

The producing composite materials of dual reinforcement in which the matrix material is aluminum reinforced with two types of ceramic particles : which are Alumina (50μm


composite materials; wear test ; Al2O3; SiC: Al

Prediction of Tigris River Stage in Qurna, South of Iraq, Using Artificial Neural Networks

Ali H. Al Aboodi; Ammar S. Dawood; Sarmad A. Abbas

Engineering and Technology Journal, 2009, Volume 27, Issue 13, Pages 2448-2456

Artificial neural networks (ANNs) with back-propagation algorithm are
performed for predicting the stage of Tigris River in Qurna city, Basrah, south of Iraq. This model was adopted to investigate the applicability of ANNs as an effective tool to simulate the river stage for short term. By using the neural network toolbox in Matlab R2007b, three models are constructed as the first experiment. Multilayer percpetron with one hidden layer is used in the architecture of network. The best model is selected according to the trial and error
procedure based on three common statistic coefficients (coefficient of correlation, root mean square error, and coefficient of efficiency). The best model from first experiment is used to predict the stage river for one, two, and three days ahead as the second experiment. Results indicated the ANNs with back-propagation algorithm are a powerful technique to predict the short term stage of Tigris River