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KURTULMUŞ, MEMDUH

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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.