A novel prediction method for gear friction coefficients with or without lubrication is developed with a computational inverse technique. A surrogate model is introduced to describe the mapping between the friction co...
详细信息
A novel prediction method for gear friction coefficients with or without lubrication is developed with a computational inverse technique. A surrogate model is introduced to describe the mapping between the friction coefficients and root stresses, according to an adaptive radial basis function. The convergence criterion for solving the friction coefficients is determined based on the calculated and measured root stresses. A nonzero friction coefficient at the pitch point, which is closely relative to the action of rolling friction, is detected using the proposed method. Results show that the gear friction coefficients decrease with an increase in rotation speed or a decline in applied torque. Moreover, the friction coefficients without lubrication are more than twice those with lubrication.
As core components of precision-guided projectiles,projectile-borne components are highly susceptible to failure or even damage in complex high-overload environments,thereby significantly compromising launch reliabili...
详细信息
As core components of precision-guided projectiles,projectile-borne components are highly susceptible to failure or even damage in complex high-overload environments,thereby significantly compromising launch reliability and ***,accurately characterizing the mechanical behavior of propellants remains challenging due to the limitations in the current internal ballistic theory and the constraints of large-scale artillery firing *** complicates the high-precision numerical modeling of projectile launch,and obstructs investigations into the failure mechanisms of projectile-borne ***,this paper identifies propellant parameters using the computationalinverse method under uncertainty,further establishes high-precision numerical models of projectile launch,and explores the failure mechanisms of projectile-borne components in complex high-overload ***,a projectile launching experiment is meticulously designed and executed to obtain the breech pressure and muzzle ***,a general simulation model is built,and the powder burn model is used to simulate the ignition and ***,the propellant parameters are effectively identified with the computationalinverse method by the combination of the experiments and simulations.A high-precision numerical model of projectile launch is modified with the parameters validated by another experiment,and the high-overload characteristics during projectile launch are thoroughly analyzed based on this ***,the high-overload characteristics of projectile-borne components are analyzed to elucidate the stress variation laws and to reveal the failure mechanisms influenced by time and spatial *** research provides an effective method for perfectly identifying propellant parameters and building high-precision numerical models of projectile ***,it provides significant guidance for the anti-high overload design and analysis of projectile-bo
A computational inverse technique is presented for identification of geometric parameters of drawbead in sheet forming processes. The explicit dynamic finite element method (FEM) is employed as the forward solver to c...
详细信息
A computational inverse technique is presented for identification of geometric parameters of drawbead in sheet forming processes. The explicit dynamic finite element method (FEM) is employed as the forward solver to calculate the maximal effective stress, maximal effective strain and maximal thinning ratio of sheet thickness for known drawbead geometric parameters. A neural network (NN) is adopted as the inverse operator to determine the geometric parameters of circular drawbead. A sample design method with the strategy of updating training sample set is developed for the fast convergence in the training process of NN model. Once the training sample set is updated, the NN structure will be optimized using the genetic algorithm (GA). The numerical examples are presented to demonstrate the efficiency of the technique.
Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1 x 1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and wit...
详细信息
Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1 x 1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and without a notch. The size of a defect and its localization in a bar change its natural frequencies. Accordingly, synthetic data was generated with the finite element method. A parametric analysis was carried out. Only one defect was taken into account and the first five natural frequencies were calculated. 495 cases were evaluated. All the input data was classified in three groups. Each one has 165 cases and corresponds to one of the three defects mentioned above. 395 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left, This procedure was followed in the cases of the plain bar and a bar with a notch. In the next stage of this work, the ANN output was optimized with ANFIS. The accuracy of the localization and classifications of the defects was improved.
暂无评论