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Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
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Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with,  an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.

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Publication Date
Sat Apr 01 2023
Journal Name
The Ocular Surface
Detecting dry eye from ocular surface videos based on deep learning
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Publication Date
Tue Nov 01 2022
Journal Name
Aci Structural Journal
Stress at Ultimate in Internally Unbonded Steel Based on Genetic Expression Programming
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Publication Date
Sat Jun 30 2012
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Phenyl Thiourea as Corrosion Inhibitor for Mild Steel in Strong Hydrochloric Acid
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The inhibitive action of Phenyl Thiourea (PTU) on the corrosion of mild steel in strong Hydrochloric acid, HCl, has been investigated by weight loss and potentiostatic polarization. The effect of PTU concentration, HCl concentration, and temperature on corrosion rate of mild steel were verified using 2 levels factorial design and surface response analysis through weight loss approach, while the electrochemical measurements were used to study the behavior of mild steel in 5-7N HCl at temperatures 30, 40 and 50 °C, in absence and presence of PTU. It was verified that all variables and their interaction were statistically significant. The adsorption of (PTU) is found to obey the Langmuir adsorption isotherm. The effect of temperature on th

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Publication Date
Sun May 01 2016
Journal Name
Istanbul Bridge Conference.- Turkey
Strengthening and Load Testing for Short Span Steel Bridge for Abnormal Loads
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The steel jetty selected for strengthening is in Baghdad city, over Tigris River, consists of 55 short spans, each of approximately 4 meters and one naviga-tional opening of 12 m. The bridge is 224 meters length and 8 meters in width. The strengthening system was designed to remove overstresses that occurred when the bridge was subjected to abnormal loads of 380 tons. A strengthening system which installed in spring 2008 was used where the main concept is to depend on added side supporting elements which impose reversal forces on the bridge to counteract most of the loads expected from the abnormal heavy loads. The bridge was load tested before and after the strengthening system was activated. The load test results indicate that the strengt

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Publication Date
Fri Jul 01 2016
Journal Name
Composite Structures
Bond behaviour between CFRP laminates and steel members under different loading rates
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Carbon fibre reinforced polymers are widely used to strengthen steel structural elements. These structural elements are normally subjected to static, dynamic and fatigue loadings during their life-time. A number of studies have focused on the characteristics of CFRP sheets bonded to steel members under static, dynamic and fatigue loadings. However, there is a gap in understanding the bonding behaviour between CFRP laminates and steel members under impact loading. This paper shows the effect of different load rates from quasi-static to 300 × 103 mm/min on this bond. Two types of CFRP laminate, CFK 150/2000 and CFK 200/2000, were used to strengthen steel joints using Araldite 420 epoxy. The results show a significant bond strength enhancemen

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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
Fear of Intimacy and its Relationship with Post-Traumatic Stress Disorder among Yazidis Women
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The current research aims to identify the fear of intimacy and post-traumatic stress disorder among Yazidi women and the correlation between them. To achieve the objectives of the research, the researcher adopted the Descutner, 1991 & (Thelen) scale, which consisted of (35) items. The researcher also adopted the post-traumatic stress disorder scale for (Davidson, 1995) translated by (Abdul Aziz Thabet), which consists of (17) items. These two scales were administered to a sample of (200) individuals. Then, the researcher analyzes the data using the Statistical Package for Social Sciences (SPSS). The results showed that the research sample of Yazidi women has a fear of intimacy. The research sample of Yazidi women is characterized by

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Publication Date
Tue Sep 01 2015
Journal Name
Journal Of Engineering
Application of Box-Behnken Method Based ANN-GA to Prediction of wt.% of Doping Elements for Incoloy 800H Coated by Aluminizing-Chromizing
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In this work , an effective procedure of Box-Behnken based-ANN (Artificial Neural Network) and GA (Genetic Algorithm) has been utilized for finding the optimum conditions of wt.% of doping elements (Ce,Y, and Ge) doped-aluminizing-chromizing of Incoloy 800H . ANN and Box-Behnken design method have been implanted for minimizing hot corrosion rate kp (10-12g2.cm-4.s-1) in Incoloy 800H at 900oC . ANN was used for estimating the predicted values of hot corrosion rate kp (10-12g2.cm-4.s-1) . The optimal wt.% of doping elements combination to obtain minimum hot corrosion rate was calculated using genetic alg

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Publication Date
Tue Oct 01 2019
Journal Name
2019 12th International Conference On Developments In Esystems Engineering (dese)
Roadway Deterioration Prediction Using Markov Chain Modeling (Wasit Governorate/ Iraq as a Case Study)
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Publication Date
Mon Jan 01 2018
Journal Name
Communications In Computer And Information Science
Automatically Recognizing Emotions in Text Using Prediction by Partial Matching (PPM) Text Compression Method
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In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

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Publication Date
Wed May 03 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks
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The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

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