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Full-Text Articles in Engineering
Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller
Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller
Electrical and Computer Engineering Faculty Research & Creative Works
Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping …
Detection And Classification Of Impact-Induced Damage In Composite Plates Using Neural Networks, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch, K. Chandrashekhara, Farhad Akhavan
Detection And Classification Of Impact-Induced Damage In Composite Plates Using Neural Networks, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch, K. Chandrashekhara, Farhad Akhavan
Electrical and Computer Engineering Faculty Research & Creative Works
Artificial neutral networks (ANN) can be used as an online health monitoring systems (involving damage assessment, fatigue monitoring and delamination detection) for composite structures owing to their inherent fast computing speeds, parallel processing and ability to learn and adapt to the experimental data. The amount of impact-induced strain on a composite structure can be found using strain sensors attached to composite structures. Prior work has shown that strain-based ANN can characterize impact energy on composite plates and that strain signatures can be associated with damage types and severity. This paper reports the extension of this approach for damage classification using …
Identification Of Cutting Force In End Milling Operations Using Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
Identification Of Cutting Force In End Milling Operations Using Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The problem of identifying the cutting force in end milling operations is considered in this study. Recurrent neural networks are used here and are trained using a recursive least squares training algorithm. Training results for data obtained from a SAJO 3-axis vertical milling machine for steady slot cuts are presented. The results show that a recurrent neural network can learn the functional relationship between the feed rate and steady-state average resultant cutting force very well. Furthermore, results for the Mackey-Glass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented here
Use Of Time Varying Dynamics In Neural Network To Solve Multi-Target Classification, S. N. Balakrishnan, J. Rainwater
Use Of Time Varying Dynamics In Neural Network To Solve Multi-Target Classification, S. N. Balakrishnan, J. Rainwater
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Several types of solutions exist for multiple target tracking. These techniques are computation-intensive and in some cases very difficult to operate online. The authors report on a backpropagation neural network which has been successfully used to identify multiple moving targets using kinematic data (time, range, range-rate and azimuth angle) from sensors to train the network. Preliminary results from simulated scenarios show that neural networks are capable of learning target identification for three targets during the time period used during training and a time period shortly after. This effective classification period can be extended by the use of networks in coordination …
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
Two artificial neural network systems are considered in a hierarchical fashion to plan the trajectory and control of a robotic arm. At the higher level of the hierarchy the neural system consists of four networks: a restricted Coulomb energy network to delineate the robot arm workspace; two standard backpropagation (BP) networks for coordinates transformation; and a fourth network which also uses BP and participates in the trajectory planning by cooperating with other knowledge sources. The control emulation process which is developed using a second neural system at a lower hierarchical level provides the correct sequence of control actions. An example …