Sahin Yildirim

Analysis of Effects of Sizes of Orifice and Pockets on the Rigidity of Hydrostatic Bearing Using Neural Network Predictor System

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.

A QP Artificial Neural Network inverse kinematic solution for accurate robot path control

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.

Statistical analysis of vehicles' vibration due to road roughness using radial basis artificial neural network

This article investigates the variation of vertical vibrations of vehicles using a Radial Basis Neural Network (RBNN). The RBNN is employed to predict desired values of amplitude of acceleration for different road conditions such as concrete, waved stone block paved and country roads. The proposed neural system is also tested for different natural frequencies and the ratios of damping. This method is conceptually straightforward, and it is also applicable to other type vehicles such as trucks.

Stress distribution of oval and circular fiber posts in amandibular premolar: a three- dimensional finite element analysis

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.

Kinematic Analysis of Cranes Using Neural Networks

Due to load uncertainties of cranes, it is necessary to find exact kinematic parameters of crane mechanisms. prograrruning techniques [41. The objective function for minimization was taken as the weight of the girder. The limitations on the stresses and the deflections induced in the girder in different load conditions were stated in the form of inequality constraints. This research is concerned with application of neural network to the kinematic analysis of a crane mechanism. The type of network investigated is a Radial Basis Neural Network (RBNN). The crane mechanism is considered as a double-rocker four-bar mechanism. Desired kinematic parameteres of the crane is found by a a software deal ing with simulation and analysis of nrecharmisms . The RBNN is employed in four parameters prediction schemes; displacement, velocity, acceleration and force. The results obtained have supported the theory that the proposed RBNN is able to predict different types of crane system.

Drilling performance analysis of drill column machine using proposed neural networks

In spite of advanced material cutting technology, there are still some problems due to unpredicted vibrations on horizontal and vertical directions on column drilling machines. This paper presents an investigation for drilling condition of drill column machines performance using proposed neural networks. The investigation is divided into two parts. First, the drill column machine is employed to analyze vibrations with steel and aluminum materials for increased drilling speeds. During the working of the system, some measuring points are indicated to analyses of drilling conditions. Finally, two types of proposed neural networks predictors are used to predict vibration variation for both cases of steel and aluminum materials of drilling systems. The experimental and simulation result is improved that radial basis neural network has superior performance to adapt experimental applications for drill column machines.

Vibration analysis of food industries mixing systems for long life using neural networks

Due to health problems on food industry, it is necessary to control exact mixing rate of some fruit juices. In this study; whole mixing systems with automation is investigated for different flow rates in the pipes. On the other hand, a robust analyzer is designed to predict real time vibrations on the system. Furthermore, from other investigations; neural networks have superior performance to predict such problems. For that reason, three types of neural networks are used to predict vibrations on different points of three tank mixing system. The results are improved that the proposed Radial Basis Neural Network (RBNN) has good performance at adapting vibration problems on mixing system. Finally, this type of neural network will be employed to analyze food industries automation systems.

Geometric and algebraic approach to the inverse kinematics of four-link manipulators

This paper presents an example of the application of geometric and algebraic approaches to the inverse kinematics problem of four-link robot manipulators. A special arm configuration of the robot manipulator is employed for solving the inverse kinematics problem by using the geometric approach. The obtained joint variables as angular positions are defined in the form of cubic polynomials. The other kinematic parameters of the joints, such as angular velocities and angular accelerations, are the time derivatives of these polynomials. It is evident that there is no definite difference between the results of the two approaches. Consequently, if an appropriate arm configuration for the geometric approach can be established, the inverse kinematics can be solved in a simpler and shorter way.

Vibration response of rotating mechanical systems using experimental techniques and artificial neural networks

A neural network predictor investigation is presented for analyzing vibration parameters of a rotating system. The vibration parameters of the system, such as amplitude, velocity, and acceleration in the vertical direction, were measured at the bearing points. The systems vibration and noise were analyzed for different working conditions. The designed neural predictor has three layers, which are input, hidden, and output layers. In the hidden layer, 10 neurons were used for this approximation. The results show that the network can be used as an analyzer of such systems in experimental applications.