Browsing by Author "Karlik B."

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    Vibrations of a beam-mass systems using artificial neural networks
    (Elsevier Ltd, 1998) Karlik B.; Özkaya E.; Aydin S.; Pakdemirli M.
    The nonlinear vibrations of an Euler-Bernoulli beam with a concentrated mass attached to it are investigated. Five different sets of boundary conditions are considered. The transcendental equations yielding the exact values of natural frequencies are presented. Using the Newton-Raphson method, natural frequencies are calculated for different boundary conditions, mass ratios and mass locations. The corresponding nonlinear correction coefficients are also calculated for the fundamental mode. The calculated natural frequencies and nonlinear corrections are used in training a multi-layer, feed-forward, backpropagation artificial neural network (ANN) algorithm. The algorithm produces results within 0.5 and 1.5% error limits for linear and nonlinear cases, respectively. By employing the ANN algorithm, computational time is drastically reduced compared with the conventional numerical techniques. © 1998 Published by Elsevier Science Ltd. All rights reserved.
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    Improved approach to the solution of inverse kinematics problems for robot manipulators
    (Elsevier Science Ltd, 2000) Karlik B.; Aydin S.
    A structured artificial neural-network (ANN) approach has been proposed here to control the motion of a robot manipulator. Many neural-network models use threshold units with sigmoid transfer functions and gradient descent-type learning rules. The learning equations used are those of the backpropagation algorithm. In this work, the solution of the kinematics of a six-degrees-of-freedom robot manipulator is implemented by using ANN. Work has been undertaken to find the best ANN configurations for this problem. Both the placement and orientation angles of a robot manipulator are used to fin the inverse kinematics solutions.
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    Determination of hardness of AA 2004 aluminium alloy under ageing conditions by means of artificial neural networks method
    (2004) Atik E.; Meric C.; Karlik B.
    As known, 2XXX and 7XXX Aluminium wrought alloys can have high strength values by means of precipitation hardening heat treatment. Determination of the precipitation hardening conditions, which can give the most suitable strength values of an alloy, requires numerous tests. But the results of this process which require long time and high cost can be obtained in a shorter time and at a lower cost with less data by means of Artificial Neural Networks method. Since this method is used, less number of experiments and therefore less data are needed. Then other values are found by means of Artificial Neural Networks (ANN) method. This paper, presents the feed forward ANN to determine hardness of alloy for different temperatures. For this purpose, a classic Back-Propagation Algorithm was used that is structure as 1:2:4.
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    Comparison of signal processing methods used for classification of EMG signals; [EMG işaretlerini siniflamada kullanilan işaret işleme tekniklerinin karşilaştirilmasi]
    (2005) Karlik B.; Koçyiǧit Y.; Fidan C.B.
    The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. This pattern consists of information of direction and speed of movement. There are different types of signal processing to find feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using Autoregressive parameters (AR), Fast Fourier Transform (FFT), and Wavelet Transform. Then these feature values are compared each other by using same classifier. The Back-Propagation Algorithm, which has 3 layer perception, was preferred as a classifier. © 2005 IEEE.
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    Artificial neural network-based prediction technique for wear loss quantities in Mo coatings
    (2006) Çetinel H.; Öztürk H.; Çelik E.; Karlik B.
    Mo coated materials are used in automotive, aerospace, pulp and paper industries in order to protect machine parts against wear and corrosion. In this study, the wear amounts of Mo coatings deposited on ductile iron substrates using an atmospheric plasma-spray system were investigated for different loads and environment conditions. The Mo coatings were subjected to sliding wear against AISI 303 counter bodies under dry and acid environments. In a theoretical study, cross-sectional microhardness from the surface of the coatings, loads, environment and friction test durations were chosen as variable parameters in order to determine the amount of wear loss. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results is reasonably good. © 2006 Elsevier B.V. All rights reserved.