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KURTULMUŞ, MEMDUH

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Author: KURTULMUŞ, MEMDUH × End date: 2018 ×

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Effects of welding parameters on penetration depth in mild steel A-TIG welding
    (SHARIF UNIV TECHNOLOGY, 2018-01-20) KURTULMUŞ, MEMDUH; Kurtulmus, M.
    A-TIG welding is a welding method in which TIG welding is conducted by covering a thin layer of activating flux on the weld bead beforehand. The most significant benefit of this process is the gain in weld penetration depth. A-TIG welds were produced on mild steel plates with TiO2 flux. The emphasis of this paper was laid upon introducing the effects of various process parameters, namely welding current, welding speed, powder/acetone ratio of the flux, arc length, and electrode angle on mild steel A-TIG welding. The weld penetration depth was measured metallographically. An optimum value was determined for each welding parameter. (C) 2019 Sharif University of Technology. All rights reserved.
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    The optimization of welding parameters for friction stir spot welding of high density polyethylene sheets
    (ELSEVIER SCI LTD, 2011) KURTULMUŞ, MEMDUH; Bilici, Mustafa Kernal; Yukler, Ahmet Irfan; Kurtulmus, Memduh
    Friction stir spot welding parameters affect the weld strength of thermoplastics, such as high density polyethylene (HDPE) sheets. The strength of a friction stir spot weld is usually determined by a lap-shear test. For maximizing the weld strength, the selection of welding parameters is very important. This paper presents an application of Taguchi method to friction stir spot welding strength of HOPE sheets. An orthogonal array, the signal to noise ratio (S/N), and the analysis of variance (ANOVA) are employed to /investigate friction stir welding parameter effects on the weld strength. From the ANOVA and the S/N ratio response graphs, the significant parameters and the optimal combination level of welding parameters were obtained. Experimental results confirmed the effectiveness of the method. (C) 2011 Elsevier Ltd. All rights reserved.
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    Artificial neural network modelling for polyethylene FSSW parameters
    (ELSEVIER SCIENCE BV, 2018) KURTULMUŞ, MEMDUH; Kurtulmus, M.; Kiraz, A.
    In a Friction Stir Spot Welding (FSSW) process, welding parameters (the tool rotational speed, tool plunge depth, and stirring time) affect the nugget formation in high-density polyethylene (HDPE) sheets. The size and microstructure of the nugget determine the resistance of the joint to outer forces. The optimization of these parameters is vital to obtaining high-quality welds. Feed forward back-propagation artificial neural network models are developed to optimize the FSSW parameters for HDPE sheets. Input variables of these models include tool rotation speed (rpm), the plunge depth (mm), and the stirring time (s) that affect lap-shear fracture load (N) output. Prediction performances of 6 models in different specifications are compared. These models differ in terms of the training dataset used (80%-100%) and the number of neurons (5-10-20) in a hidden layer. The best prediction performances are obtained using 20 neurons in a hidden layer in both training dataset. There is good agreement between developed models' predictions and the experimental data. (c) 2018 Sharif University of Technology. All rights reserved.
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    PublicationOpen Access
    Artificial Neural Network Modelling for Prediction of SNR Effected by Probe Properties on Ultrasonic Inspection of Austenitic Stainless Steel Weldments
    (DE GRUYTER POLAND SP ZOO, 2018-05-28) KURTULMUŞ, MEMDUH; Kurtulmus, Memduh
    Many austenitic stainless steel components are used in the construction of nuclear power plants. These components are joined by different welding processes, and radiation damages occur in the welds during the service life of the plant. The plants are inspected periodically with ultrasonic test methods. Many ultrasonic inspection problems arise due to the weld metal microstructure of austenitic stainless steel weldments. The present research was conducted in order to describe the affects of probe angle and probe frequency of both transversal and longitudinal wave probes on detecting the defects of austenitic stainless steel weldments. Feed forward back propagation artificial neural network (ANN) models have been developed for predicting signal to noise ratio (SNR) of transversal and longitudinal wave probes. Input variables that affect SNR output in these models are welding angle, probe angle, probe frequency and sound path. Of the experimental data, 80% is used for a training dataset and 20% is used for a testing dataset with 10 neurons in hidden layers in developed ANN models. Mean absolute error (MAE) and mean absolute percentage error (MAPE) types are calculated as 0.0656 and 16.28%, respectively, to predict performance of ANN models in a transversal wave probe. In addition, MAE and MAPE are calculated as 0.0478 and 18.01%, respectively, for performance in a longitudinal wave probe.