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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Use of Neural Networks for the Stiffness Calculation of a Spur Gear Transmission
 Planka, Michal ; Krpalek, David (referee) ; Lošák, Petr (advisor)
The aim of this master's thesis is to build artificial neural network that is able to calculate varying single tooth-pair mesh stiffness of spur gear for given input parameters. The training set for this network was determined by computational modelling by finite element method. Therefore, creating of computational model and mesh stiffness calculating were a partial aim of this thesis. Input parameters for stiffness calculation were number of driving and driven gear teeth and gear loading. Creating of computational model and performing series of simulations was followed by creating artificial neural network. Multilayer neural network with backpropagation training was chosen as a type of the network. Created neural network is sufficiently efficient and can determine varying mesh stiffness in input set range for learned input parameters and for values of parameters that are not included in training set as well. This neural network can be used for varying single tooth-pair mesh stiffness estimation in input set range.
Detailed record
Use of Neural Networks for the Stiffness Calculation of a Spur Gear Transmission
 Planka, Michal ; Krpalek, David (referee) ; Lošák, Petr (advisor)
The aim of this master's thesis is to build artificial neural network that is able to calculate varying single tooth-pair mesh stiffness of spur gear for given input parameters. The training set for this network was determined by computational modelling by finite element method. Therefore, creating of computational model and mesh stiffness calculating were a partial aim of this thesis. Input parameters for stiffness calculation were number of driving and driven gear teeth and gear loading. Creating of computational model and performing series of simulations was followed by creating artificial neural network. Multilayer neural network with backpropagation training was chosen as a type of the network. Created neural network is sufficiently efficient and can determine varying mesh stiffness in input set range for learned input parameters and for values of parameters that are not included in training set as well. This neural network can be used for varying single tooth-pair mesh stiffness estimation in input set range.
Detailed record

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