Person: KURTULMUŞ, MEMDUH
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
KURTULMUŞ
First Name
MEMDUH
Name
8 results
Search Results
Now showing 1 - 8 of 8
Publication Open 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.Publication Metadata only 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, MemduhFriction 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.Publication Metadata only Activated flux TIG welding of austenitic stainless steels(ICE PUBLISHING, 2020) KURTULMUŞ, MEMDUH; Kurtulmus, MemduhThe tungsten (W) inert gas (TIG) welding process that is applied with an active flux deposited on the workpiece surface just before welding is called activated flux TIG (A-TIG) welding. Layer deposition can be achieved by brushing or spraying over the surface, and welding is carried out after the surface dries out. This process has shown that it is possible to increase weld penetration and productivity up to three times higher or more compared with the TIG process in steels. In this review paper, A-TIG welding applications of steels were examined. The chemical composition and thickness of the flux and welding parameters (welding current, welding speed, arc length and shielding gas composition and its flow rate) affect the weld geometry. The activated flux welding mechanisms, effects of flux and welding parameters on weld geometry and microstructure and properties of A-TIG welds were explained.Publication Metadata only 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.Publication Metadata only An evaluation on the principles of tribology and usage in wear applications(Güven Plus Group Inc. Publications: 23/2022 20 DECEMBER 2022, 2022-01-01) YALÇINKAYA, SENAİ; KURTULMUŞ, MEMDUH; DOĞAN E., YALÇINKAYA S., KURTULMUŞ M.Publication Metadata only The effects of undercut geometry on the static stress concentration factor of welds(ICE PUBLISHING, 2021) KURTULMUŞ, MEMDUH; Kurtulmus, Memduh; Dogan, EzgiUndercutting is a welding defect that appears as a groove in the base metal directly along the edges of the weld metal. It is inevitable in fillet and butt joints if improper welding parameters are used in the operation. It is a discontinuity in the welding that produces stress concentration and lowers the strength of the weld. The stress concentration factor of an undercut is due to the reinforcement angle, undercut width, undercut depth and undercut root radius. In this study, 20 mm thick mild steel plates were welded by automatic gas metal arc welding with the carbon dioxide (CO2) shielding gas process. A single-V butt joint was obtained after welding. Before welding, 30 degrees groove angles were obtained by milling on the longitudinal side of each workpiece. Two plates were welded with several passes. After welding, the weldment was tested with a radiographic non-destructive testing process. A defect-free weldment was obtained. Then, standard tensile test samples were machined from the weldment. A groove was drilled in the heat-affected zone, adjacent to the weld metal boundary on every tensile test sample. Each groove resembled an undercut. The length, root radius and depth of grooves were varied. Then, the samples were broken on a tensile test machine. From the test results, the static stress concentration factor of each groove was calculated. The effects of groove geometry on stress concentration factors were determined.Publication Open 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, MemduhMany 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.Publication Metadata only Experimental investigation and optimization of welding parameters on weld strength in friction stir spot welding of aluminum using Taguchi experimental design(ICE PUBLISHING, 2020) KURTULMUŞ, MEMDUH; Kurtulmus, MemduhNineteen years ago, the automotive industry started using the friction stir spot welding (FSSW) process in joining metallic parts. The popularity of this welding process became higher every year. In this study, aluminum (Al) 1020 sheets of 2mm thickness were joined with the FSSW process. The effects of FSSW parameters (plunge depth, tool rotation speed and dwell time) on the mechanical properties of weldments were investigated. The mechanical performance of the welds was evaluated by using the lap-shear tensile test. The optimization was done by using the Taguchi method. 'The-higher-the-better' quality control characteristic using the analysis of variance (Anova) method was applied to determine the optimum welding parameters. The signal-to-noise ratio was computed to calculate the optimal process parameters. The percentage contributions of each parameter were validated by using the Anova technique. The experimental results were analyzed by using the Minitab 17 software. The tool rotation speed was found as the dominant welding parameter on the weld strength of aluminum 1020 sheets for the FSSW process. The weld that had been produced with the optimum welding parameters gave a 23% higher fracture load than the initial welds.