Abdorrasoul Mayyahi; Aghil Yousefikoma; Ali Rangin Kaman; Hesam Maleki
Abstract
An autonomous underwater vehicle (AUV) with less noise and vortices as well as efficient power consumption, is pursued in this research by inspiration of shark swimming. Design, hydrodynamic analysis, modeling, fabrication, navigation, and control of this novel AUV is the main goal of this research. ...
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An autonomous underwater vehicle (AUV) with less noise and vortices as well as efficient power consumption, is pursued in this research by inspiration of shark swimming. Design, hydrodynamic analysis, modeling, fabrication, navigation, and control of this novel AUV is the main goal of this research. Detailed explanation of the test and experiment with a brief overview on fabrication are provided. The transfer function of the system has been extracted from the experimental data. The transfer function is then employed for dynamic analysis and control system development. Zigler-Nickols method is used to predetermine the PID control coefficients. Consequently, small modifications have been done by trial and error. Trajectory control in a 10 cm off the wall and in a 20 cm band in a large swimming pool has been examined by a 3 DOF AUV.
Vahid Norouzifard; Aghil Yousefikoma
Abstract
The built up layer thickness in secondary deformation zone is one of the important parameters in metal cutting process. The built up layer (BUL) is formed in second deformation zone near the tool-chip interface in the back of the chip. This parameter influences the tool life and machined surface quality. ...
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The built up layer thickness in secondary deformation zone is one of the important parameters in metal cutting process. The built up layer (BUL) is formed in second deformation zone near the tool-chip interface in the back of the chip. This parameter influences the tool life and machined surface quality. This BUL should not be confused with the built up edge (BUE). The deformation of the BUL in the secondary shear zone is a stable and continues process; leading to an uniform thickness of the BUL along the chip's back but the deformation of the BUE is an unstable process in front of the tool edge. Numerical simulation is a suitable method for determination of temperature, stress and strain distribution in metal cutting since it dose not suffer the analytical methods limitations and experimental methods cost. In this paper a new method is presented to calculate the built up layer thickness in secondary deformation zone using finite element simulation of orthogonal metal cutting process. There are two main concepts about chip separation mechanisms from work piece, i. e. crack propagation and pour deformation without crack. In the present work chip formation process is assumed as a pour plastic deformation, considering second chip separation mechanism. There is no separation criterion in the simulations based on pour deformation, but Adaptive remeshing is performed during simulation to avoid the difficulties associated with deformation-induced element distortion. An updated Lagrangian finite element model of two-dimensional orthogonal cutting process is developed. This model is meshed using 4-node plain strain elements. Thermo-mechanical coupled analysis, with adaptive remeshing is performed by LS-DYNA finite element code. Johnson-Cook material model is used for determination of the work piece material flow stress and the cutting tool is assumed as a rigid body. An updated coulomb friction law is used to describe friction condition in tool-chip interface. The temperature and equivalent strain distribution diagrams in cutting zone are shown at various cutting speeds. The built up layer thickness in various cutting speed are also calculated by equivalent strain gradient in second deformation zone. The numerical calculated tool average temperatures and the built up layer thicknesses in various cutting speeds are compared with the experimental data given in literature and good agreement is observed between them.
Hossein Shahi; Aghil Yousefikoma; Ali Reza Mehrabian
Abstract
A solution to the problem of identification and control of smart structures is presented in this paper. Smart structures with build-in sensors and actuators can actively and adaptively change their physical geometry and properties. As a particular example, a representative dynamic model of a typical ...
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A solution to the problem of identification and control of smart structures is presented in this paper. Smart structures with build-in sensors and actuators can actively and adaptively change their physical geometry and properties. As a particular example, a representative dynamic model of a typical fighter vertical tail, identified as the smart fin, is considered. Piezoelectric patches, which are mounted on the vertical tail, are employed as actuator in the model. The Frequency Response Function (FRF) of the smart fin is obtained from experiment. The corresponding transfer function is then derived using classic system identification (ID) techniques, using MATLAB® system identification toolbox, which is verified with the experimental data. The model obtained using system ID is then used to tune an optimal PID controller to reduce the vibration of the smart structure. To this end, several cost functions are defined and optimized by a genetic algorithm. Next, the obtained controllers are compared with each other and a suitable one is chosen as the system’s controller. Finally, It is shown in simulations that the designed controller is able to reduce the vibration of the smart fin very well.
