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Force analysis of bearing on a modified mechanism using proposed recurrent hybrid neural networks
SAHIN YILDIRIM,İkbal Eski,Menderes Kalkat 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.7
Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distribution on the bearing systems of mechanisms. A proposed neural network was developed and designed to analyze force distribution on the bearings of a four bar mechanism. The proposed neural network has three layers: input layer, output layer and hidden layer. The hidden layer consists of a recurrent structure to keep dynamic memory for later use. The mechanism is an extended version of a four-bar mechanism. Two elements, spring and viscous, are employed to overcome big force problem on the bearings of the mechanism. The results of the proposed neural network give superior performance for analyzing the forces on the bearings of the four-bar mechanism undergoing big forces and high repetitive motion tracking. This continuation of simulation analysis of bearings should be a benefit to bearing designers and researchers of such mechanisms.
A QP Artificial Neural Network Inverse Kinematic Solution for Accurate Robot Path Control
Yildirim Sahin,Eski Ikbal The Korean Society of Mechanical Engineers 2006 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.20 No.7
In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot's kinematics.
Design of Neural Networks Model for Transmission Angle of a Modified Mechanism
Yildirim Sahin,Erkaya Selcuk,Su Siikrii,Uzmay ibrahim The Korean Society of Mechanical Engineers 2005 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.19 No.10
This paper discusses Neural Networks as predictor for analyzing of transmission angle of slider-crank mechanism. There are different types of neural network algorithms obtained by using chain rules. The neural network is a feedforward neural network. On the other hand, the slider-crank mechanism is a modified mechanism by using an additional link between connecting rod and crank pin. Through extensive simulations, these neural network models are shown to be effective for prediction and analyzing of a modified slider-crank mechanism's transmission angle.
Yildirim, Merve,Sumnu, Gulum,Sahin, Serpil 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.6
In this study, the effects of the double emulsification method on the rheological properties, particle size, and stability of low-fat mayonnaise were studied. Different water-phase-to-oil ratios (2:8 and 4:6) of primary emulsions and different stabilizer types (sodium caseinate, xanthan gum, and lecithin-whey protein concentrate) were used to produce double-emulsified mayonnaise. As a control sample, mayonnaise was prepared conventionally. Sodium caseinate was found to be the most efficient stabilizer. In the presence of sodium caseinate, the stability and apparent viscosity of double-emulsified mayonnaise increased but their particle sizes decreased. It was found that flow behavior of double-emulsified and conventionally prepared mayonnaise could be described by the power law model. The double-emulsified mayonnaise samples were not different from the control samples in terms of stability and particle size. In addition, using the double emulsion method, it was possible to reduce the oil content of mayonnaise to 36.6%.
Merve Yildirim,Gulum Sumnu,Serpil Sahin 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.6
In this study, the effects of the double emulsification method on the rheological properties, particle size, and stability of low-fat mayonnaise were studied. Different water-phase-to-oil ratios (2:8 and 4:6) of primary emulsions and different stabilizer types (sodium caseinate, xanthan gum, and lecithin-whey protein concentrate) were used to produce double-emulsified mayonnaise. As a control sample, mayonnaise was prepared conventionally. Sodium caseinate was found to be the most efficient stabilizer. In the presence of sodium caseinate, the stability and apparent viscosity of doubleemulsified mayonnaise increased but their particle sizes decreased. It was found that flow behavior of double-emulsified and conventionally prepared mayonnaise could be described by the power law model. The double-emulsified mayonnaise samples were not different from the control samples in terms of stability and particle size. In addition, using the double emulsion method, it was possible to reduce the oil content of mayonnaise to 36.6%.
Fazil Canbulut,cem Sinanoglu,sahin Yildirim 대한기계학회 2004 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.18 No.3
This paper presents a neural network predictor for analysing rigidity variations of hydrostatic bearing system. The designed neural network has feedforward structure with three layers . The layers are input layer, hidden layer and output layer. Two main parameter could be considered for hydrostatic bearing system. These parameters are the size of bearing pocket and the orifice dimension. Due to importancy of these parameters, it is necessary to analyse with a suitable optimisation method such as neural network. As depicted from the results, the proposed neural predictor exactly follows experimental desired results.<br/>
Canbulut, Fazil,Sinanoglu, Cem,Yildirim, Sahin The Korean Society of Mechanical Engineers 2004 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.18 No.3
This paper presents a neural network predictor for analysing rigidity variations of hydrostatic bearing system. The designed neural network has feedforward structure with three layers. The layers are input layer, hidden layer and output layer. Two main parameter could be considered for hydrostatic bearing system. These parameters are the size of bearing pocket and the orifice dimension. Due to importancy of these parameters, it is necessary to analyse with a suitable optimisation method such as neural network. As depicted from the results, the proposed neural predictor exactly follows experimental desired results.
Halil Ibrahim Kilin,Tugrul Aslan,Sahin Yildirim,Emir Esim,Ozgur Er,Kerem Kili 대한치과보철학회 2013 The Journal of Advanced Prosthodontics Vol.5 No.4
PURPOSE The aim of the present study was to evaluate the effects of posts with different morphologies on stress distribution in an endodontically treated mandibular premolar by using finite element models (FEMs). MATERIALS AND METHODS A mandibular premolar was modeled using the ANSYS software program. Two models were created to represent circular and oval fiber posts in this tooth model. An oblique force of 300 N was applied at an angle of 45° to the occlusal plane and oriented toward the buccal side. von Mises stress was measured in three regions each for oval and circular fiber posts. RESULTS FEM analysis showed that the von Mises stress of the circular fiber post (426.81 MPa) was greater than that of the oval fiber post (346.34 MPa). The maximum distribution of von Mises stress was in the luting agent in both groups. Additionally, von Mises stresses accumulated in the coronal third of root dentin, close to the post space in both groups. CONCLUSION Oval fiber posts are preferable to circular fiber posts in oval-shaped canals given the stress distribution at the post-dentin interface.
Er, Ozgur,Kilic, Kerem,Esim, Emir,Aslan, Tugrul,Kilinc, Halil Ibrahim,Yildirim, Sahin The Korean Academy of Prosthodonitics 2013 The Journal of Advanced Prosthodontics Vol.5 No.4
PURPOSE. The aim of the present study was to evaluate the effects of posts with different morphologies on stress distribution in an endodontically treated mandibular premolar by using finite element models (FEMs). MATERIALS AND METHODS. A mandibular premolar was modeled using the ANSYS software program. Two models were created to represent circular and oval fiber posts in this tooth model. An oblique force of 300 N was applied at an angle of $45^{\circ}$ to the occlusal plane and oriented toward the buccal side. von Mises stress was measured in three regions each for oval and circular fiber posts. RESULTS. FEM analysis showed that the von Mises stress of the circular fiber post (426.81 MPa) was greater than that of the oval fiber post (346.34 MPa). The maximum distribution of von Mises stress was in the luting agent in both groups. Additionally, von Mises stresses accumulated in the coronal third of root dentin, close to the post space in both groups. CONCLUSION. Oval fiber posts are preferable to circular fiber posts in oval-shaped canals given the stress distribution at the postdentin interface.