In hot deformation, the flow stress curves of steels always present as two typical types: at relatively high temperature and low strain rate, the flow stress may first increase and then attain a steady value without reaching an obvious peak stress; in other situations, the flow stress decreases after reaching peak stress and then attains a steady value. A new phenomenological model,described by a sine-function equation, is proposed to define the relationship between flow stress and deformation parameters. A series of isothermal compressions for a carbon steel were carried out, as a case study, to obtain basic experimental data.Parameters of the new model were sequentially determined. The predicted results of the proposed model were compared with actual measured data. Good accuracy was found in the standard statistical parameters of correlation coefficient, root mean square error, and average absolute relative error with the values of 0.935, 7.137 MPa and 4.352%, respectively. Discussion of applications of different models in finite-element simulation demonstrated the benefit of the new model. When comparing the simulation results of three different deformation patterns with large strain, the new model showed 10%–20% lower predicted forming load than the original Arrhenius equation, and better applicability and reliability than modified Arrhenius equations. 相似文献
This paper concerns the hardness of approximating the closest vector in a lattice with preprocessing in l1norm,and gives a polynomial time algorithm for GapCVPPγin l1norm with gapγ=O(n/log n).The gap is smaller than that obtained by simply generalizing the approach given by Aharonov and Regev.The main technical ingredient used in this paper is the discrete Laplace distribution on lattices which may be of independent interest. 相似文献
An axial piston pump is a key component that plays the role of the “heart” in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and catastrophic safety consequences. Several vibration-based machine learning methods have been developed to detect and diagnose faults of axial piston pumps. However, most of these intelligent diagnosis methods use single-sensor vibration data to monitor the pump health states. Additionally, the diagnostic accuracy is unacceptable in most situations due to the complex pump structure and limited sensor information. Therefore, this study proposes a multi-sensor fusion method to improve the fault diagnosis performance of axial piston pumps. The convolutional neural network receives three channels of vibration data and makes the final diagnosis through information fusion at the decision level. The proposed decision fusion method is evaluated on the classification task of leakage levels of an actual axial piston pump. The experimental results show that the proposed method improves the classification accuracy by adjusting the probability distribution of classification according to the learned weight matrix.