Junsong Jin

High-temperature deformation behavior and processing map of 7050 aluminum alloy re]20101008

The high-temperature deformation behavior and processing map of 7050 aluminum alloy were investigated by tensile tests conducted at various temperatures (340, 380, 420, and 460 °C) with various strain rates of 10−4, 10−3, 10−2, and 0.1 s−1. The results show that the instability region with a peak power dissipation efficiency of 100 % occurs at the low deformation temperature region of 340 °C to 380 °C and high strain rates (>10−3 s−1). The 7050 aluminum alloy exhibited a continuous dynamic recrystallization domain with power dissipation efficiency of 35% to 60 % in the deformation temperature range of 410 °C to 460 °C and the strain rate range of 10−4–10−3 s−1. The domain with a power dissipation efficiency of 35 % to 50 % occurring at high deformation temperatures and strain rates was interpreted to represent dynamic recovery. Dynamic recovery and continuous dynamic recrystallization provide chosen domains for excellent hot workability.

Optimization of gear blank preforms based on a new R-GPLVM model utilizing GA-ELM

A novel R-GPLVM is proposed to screen out critical dimensions of the preform.A newly GA-ELM framework seamlessly integrated with R-GPLVM is proposed.Discussions demonstrate that Gaussian kernel function has the higher accuracy.The relevant parameters of ELM are optimized with the improved performance.Engineering applications and FEM validate the feasibility of the proposed method. The determination of the key dimensions of gear blank preforms with complicated geometries is a highly nonlinear optimization task. To determine critical design dimensions, we propose a novel and efficient dimensionality reduction (DR) model that adapts Gaussian process regression (GPR) to construct a topological constraint between the design latent variables (LVs) and the regression space. This procedure is termed the regression-constrained Gaussian process latent variables model (R-GPLVM), which overcomes GPLVMs drawback of ignoring the regression constrains. To determine the appropriate sub-manifolds of the high-dimensional sample space, we combine the maximum a posteriori method with the scaled conjugate gradient (SCG) algorithm. This procedure can estimate the coordinates of preform samples in the space of LVs. Numerical experiments reveal that the R-GPLVM outperforms the pure GPR in various dimensional spaces, when the proper hyper-parameters and kernel functions are solved for. Results using an extreme learning model (ELM) obtain a better prediction precision than the back propagation method (BP), when the dimensions are reduced to seven and a Gaussian kernel function is adopted. After the seven key variables are screened out, the ELM model will be constructed with realistic inputs and obtains improved prediction accuracy. However, since the ELM has a problem with validity of the prediction, a genetic algorithm (GA) is exploited to optimize the connection parameters between each network layer to improve the reliability and generalization. In terms of prediction accuracy for testing datasets, GA has a better performance compared to the differential evolution (DE) approach, which motivates the choice to use the genetic algorithm-extreme learning model (GA-ELM). Moreover, GA-ELM is employed to measure the aforementioned DR using engineering criteria. In the end, to obtain the optimal geometry, a parallel selection method of multi-objective optimization is proposed to obtain the Pareto-optimal solution, while the maximum finisher forming force (MFFF) and the maximum finisher die stress (MFDS) are both minimized. Comparative analysis with other numerical models including finite element model (FEM) simulation is conducted using the GA optimized preform. Results show that the values of MFFF and MFDS predicted by GA-ELM and R-GPLVM agree well with the experimental results, which validates the feasibility of our proposed methods.

Microstructure evolution and modeling of 2024 aluminum alloy sheets during hot deformation under different stress states

In this work, specimens of the 2024 aluminum alloy sheet were compressed and stretched along the original rolling direction at elevated temperatures. The microstructure evolution was investigated by characterizing the metallographic structures via electron backscattered diffraction technology before and after deformation. It was found that while recrystallization occurred in the compressed specimens, it was not observed to the same extent in the stretched specimens. This difference in the grain morphology has been attributed to the different movement behaviors of the grain boundaries, i.e., their significant migration in the compression deformation and the transformation from low-angle to high-angle boundaries observed mainly during tension deformation. The empirical model, which can describe the grain size evolution during compression, is not suitable in the case of tension, and therefore, a new model which ignores the detailed recrystallization process has been proposed. This model provides a description of the grain size change during hot deformation and can be used to predict the grain size in the plastic deformation process.

Stamping-Forging Processing of Sheet Metal Parts

SFP is a combined metal forming technology of stamping and forging for sheet metal parts. In an SFP, generally, stamping or drawing is used to form the spatial shape of the part first, and followed by a bulk forming employed to form the local thickened feature. It is suitable for making sheet metal parts which have local thickened feature, such as single or double layers cup parts with thickened inner or outer wall, disc-like parts with thickened rim, etc.