Parameters Optimization of γ-Ti-46.6Al-1.4Mn-2Mo Alloy by Hot-Pressing Sintering and its Microstructures

Li, Xu Guang; Lu, Hui; Wang, Pan Pan

doi:10.4028/www.scientific.net/kem.551.92

Cited by 1 publication

(1 citation statement)

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“…With the continuous advancement of machine learning, data-mining algorithms, and computer technology, some scholars have tried to apply ANN [17], BP neural networks [18,19], GA-BP neural networks [20], BAS-BP neural networks, particle swarm optimization (PSO)-BP neural networks, and other machine-learning algorithms for flow stress prediction, wear prediction, and parameter optimization. Among them, the beetle antennae search (BAS) algorithm was proposed by Jiang and Li et al [21,22], which is a bionic heuristic algorithm based on the biological behavior of long-bearded longhorns to detect and find food.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis and Parameter Optimization of Wear Characteristics of Titanium Alloy Cross Wedge Rolling Die

Peng

Huang

et al. 2021

Metals

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Cross wedge rolling has the advantages of high production efficiency, good product quality, high material utilization, environmental protection, and low cost. It is one of the best processing methods for producing shaft blanks. In this paper, a cross wedge rolling die of TC4 titanium alloy is studied. Based on the Archard wear model, a modified model suitable for cross wedge rolling die wear analysis is derived through finite element simulation. Then, the modified Archard wear model is imported into Deform-3D software for finite element analysis. Orthogonal experimental design is used to combine and analyze different process parameters. Finally, the beetle antennae search (BAS)-genetic algorithm (GA)-back propagation neural network (BPNN) algorithm is used to predict the degree of die wear and to optimize the simulation parameters, which can acquire the process parameters that have the least impact on die wear. The results show that the wear distributions of cross wedge rolling tools is uneven. In general, the most serious areas are basically concentrated in the wedge-shaped inclined plane and rectangular edge lines. The reason is that the tangential force and radial force received by the die are relatively large, which leads to increased wear. Moreover, the temperature change is most severe on the wedge-shaped ridge line. When in contact with the workpiece, the temperature rises sharply, which makes the local temperature rise, the mold hardness decrease, and the wear accelerate. Through response surface method (RSM) analysis, it is concluded that the deformation temperature is the main factor affecting wear depth, followed by the forming angle, and that there is an interaction between the two factors. Finally, the feasibility of the BAS-GA-BP algorithm for optimizing the wear behavior of dies is verified, which provides a new process parameter optimization method for the problem of die wear in the cross wedge rolling process.

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