Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : neural networks


Design a System to Estimate the Road Construction Project Preliminary Equipment Requirements in the Design Stage

Raid S. Abd Ali; Tareq A. khaleel; Shealan H. Ameen

Engineering and Technology Journal, 2016, Volume 34, Issue 13, Pages 554-565

Road construction projects in Iraq require a developmental study of the planning process toward building computerized management systems. In this thesis, a management system has been built, based on artificial neural networks and genetic algorithms. The proposed software estimates the optimal number of equipment, machineries, and relevance instruments required according to progress table of the work during the proposed implementation period of the project. Artificial neural network systems have been adopted to build models to predict the productivity of the equipment used in road construction projects, based on the factors that affecting the productivity of these mechanisms. By implementing the system and simulating at road project, several conclusions have been conducted. One of the most important conclusions is that the optimal distribution of the numbers and types of machineries used in road construction has a significant impact on the time of implementation of project.

Effect of Some Vegetables (Carrots, Onion, Parsley, and Red radish) on Corrosion Behavior of Amalgam Dental Filling in Artificial Saliva

Slafa Ismael Ibrahim; Nemir Ahmed Al-Azzawi; Shatha Mizhir Hasan; Hussein H. Karim; Ammar M. M. Al-Qaissi; Ahmed Chyad Kadhim; Mehdi Munshid Shellal; Sinan Majid Abdul Satar; Wahid S. Mohammad; Assad Oda Jassim; Khalid salem Shibib; Karema Assi Hamad; Haqui Ismael Qatta; Hayder Hadi Abbas; Kanaan A. Jalal; Hussain Kassim Ahmad; Makram A. Fakhri; Mohanned M.H. AL-Khafaji; Hussam Lefta Alwan; Baraa M.H. Albaghdadi

Engineering and Technology Journal, 2014, Volume 32, Issue 5, Pages 1216-1226

This work involves study corrosion behavior of amalgam in presence of some vegetables including (Carrots, Onion, Parsley, and Red radish) which were chosen because they require mastication process by teeth and taking enough time that make them in a contact with amalgams filling in artificial saliva.
The corrosion parameters were interpreted in artificial saliva at pH (5.1) and (37±1oC) by adding (50 ml/l) of vegetable juice to artificial saliva, which involve corrosion potential (Ecorr), corrosion current density (icorr), Cathodic and anodic Tafel slopes (bc & ba ) and polarization resistance, the results of (Ecorr) and (icorr) indicate that the medium of saliva and (50 ml/l) onion is more corrosive than the other media. Cathodic and anodic tafel slopes were used to calculate the polarization resistance (Rp) to know which medium more effective on amalgam of dental filling, this study shows that the increasing in polarization resistance through the decreasing in corrosion rate values, the results of (Rp) take the sequence:
Rp:( saliva+ parsley) >( saliva+ red radish)> saliva>(saliva+ carrots) >(saliva+ onion).
While corrosion rates (CR ) take the sequence:
CR: (Saliva+Parsley) Keywords

Amalgam
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Corrosion in saliva
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Potentiostatic measurements

Roughness Assessment for Machined Surfaces in Turning Operation Using Neural Network

Mohanned M.H. AL-Khafaji; Hussam Lefta Alwan; Baraa M.H. Albaghdadi

Engineering and Technology Journal, 2014, Volume 32, Issue 5, Pages 1331-1344

Feed forward artificial neural network has been applied to predict the quality of turned surfaces for two types of coated carbide inserts. Four networks were proposed for each insert. The networks have been trained and tested using a former experimental data. The input data, represented by cutting parameter values, and output data, represented by surface roughness, were fed into the network model. Each network has three layers adopted for prediction. The first one is the input layer which involves cutting parameters: cutting speed, feed rate, and depth of cut; the second layer is hidden layer consisting of two hidden layers. The third layer of the network is the output layer which gives the surface roughness value. Levenberg - Marquardt algorithm is used in the back-propagation algorithm to train these networks. The best result was obtained for networks which have (12) neurons in the first hidden layer and (9) neurons in the second hidden layer. These networks had given R^2=0.9902 and mean square error = 0.0033 for the first insert, whereas, for the second insert, R^2=0.9892 and mean square error = 0.0023. These networks were used to predict the optimum cutting parameters which give minimum surface roughness.