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 Recursive functions (2)
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 Metal cutting (1)
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 Force measurement (1)
 Milling (machining) (1)
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 Cutting force identification (1)
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Articles 1  5 of 5
FullText Articles in Mechanical Engineering
Nc Milling Error Assessment And Tool Path Correction, Yunching Huang, James H. Oliver
Nc Milling Error Assessment And Tool Path Correction, Yunching Huang, James H. Oliver
Mechanical Engineering Conference Presentations, Papers, and Proceedings
A system of algorithms is presented for material removal simulation, dimensional error assessment and automated correction of Þveaxis numerically controlled (NC) milling tool paths. The methods are based on a spatial partitioning technique which incorporates incremental proximity calculations between milled and design surfaces. Hence, in addition to realtime animated Þveaxis milling simulation, milling errors are measured and displayed simultaneously. Using intermediate error assessment results, a reduction of intersection volume algorithm is developed to eliminate gouges on the workpiece via tool path correction. Finally, the view dependency typical of previous spatial partitioningbased NC simulation methods is overcome by a contour display ...
A Recursive Least Squares Training Algorithm For Multilayer Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
A Recursive Least Squares Training Algorithm For Multilayer Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Recurrent neural networks have the potential to perform significantly better than the commonly used feedforward neural networks due to their dynamical nature. However, they have received less attention because training algorithms/architectures have not been well developed. In this study, a recursive least squares algorithm to train recurrent neural networks with an arbitrary number of hidden layers is developed. The training algorithm is developed as an extension of the standard recursive estimation problem. Simulated results obtained for identification of the dynamics of a nonlinear dynamical system show promising results.
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 3axis 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 steadystate average resultant cutting force very well. Furthermore, results for the MackeyGlass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented here
Advanced Fuzzy Logic Controllers And SelfTuning Strategy , ShouHeng Huang
Advanced Fuzzy Logic Controllers And SelfTuning Strategy , ShouHeng Huang
Retrospective Theses and Dissertations
This study has concentrated on fuzzy logic controllers from the basic aspects to an advanced selftuning strategy. Fuzzy logic provides a very good technique for knowledge representation which makes it possible to incorporate the experience of human operators in the design of controllers;The basic concepts of fuzzy set theory, fundamental definitions of fuzzy logic, and basic structure of fuzzy logic controllers are introduced and a guideline for building the fuzzy rulebased system is developed. The rule development and adjustment strategies for fuzzy logic controllers are presented and experimentally identified. The fuzzy logic control system is analyzed on a linguistic ...
Spline Network Modeling And Fault Classification Of A Heating Ventilation And AirConditioning System , Mathew Scaria Chackalackal
Spline Network Modeling And Fault Classification Of A Heating Ventilation And AirConditioning System , Mathew Scaria Chackalackal
Retrospective Theses and Dissertations
A spline network, that is an alternative to artificial neural networks, is introduced in this dissertation. This network has an input layer, a single hidden layer, and an output layer. Spline basis functions, with small support, are used as the activation functions. The network is used to model the steady state operation of a complex Heating Ventilation and Airconditioning (HVAC) system. Real data was used to train the spline network. A neural network was also trained on the same set of data. Based on the training process, it is possible to conclude that when compared to artificial neural networks, the ...