Study on the Influence of Injection Velocity on the Evolution of Hole Defects in Die-Cast Aluminum Alloy DOI Open Access

Hanxue Cao,

Qiang Zhang,

Wenna Zhu

и другие.

Materials, Год журнала: 2024, Номер 17(20), С. 4990 - 4990

Опубликована: Окт. 12, 2024

Aluminum alloy die casting has achieved rapid development in recent years and been widely used all walks of life. However, due to its high pressure high-speed technological characteristics, avoiding hole defects become a problem great significance aluminum production. In this paper, the filling visualization dynamic characterization experiment was innovatively developed, which can directly study analyze influence different injection rates on formation evolution flow patterns gas-induced defects. As speed increased from 1.0 m/s 1.5 m/s, average porosity 7.49% 9.57%, marking an increase number size pores. According comparison with Anycasting, simulation results show that liquid metal when front vs. previous rate caused fractures at same distance. Therefore, degree broken splash is more serious. Combined analysis transport mechanics, fracturing wall-attached jet effect process. It difficult for adhere type wall order fuse subsequent form cavity With velocity, microgroup volume formed via breakage decreases; thus air entrapment increases, finally leading

Язык: Английский

Local stress/strain field analysis of die-casting Al alloys via 3D model simulation with realistic defect distribution and RVE modelling DOI
Jian Yang, Bo Liu, Dongwei Shu

и другие.

Engineering Failure Analysis, Год журнала: 2024, Номер unknown, С. 109104 - 109104

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

5

Elongation Prediction of Die-Cast Aluminum Alloy Based on 3D Convolutional Neural Network Model DOI
Jinsheng Zhang,

Zhen Zheng,

Xiaolian Zhao

и другие.

SAE International Journal of Materials and Manufacturing, Год журнала: 2025, Номер 18(4)

Опубликована: Апрель 9, 2025

<div>This study aims to predict the impact of porosities on variability elongation in casting Al-10Si-0.3Mg alloy using machine learning methods. Based dataset provided by finite element method (FEM) modeling, two algorithms including artificial neural network (ANN) and 3D convolutional (3D CNN) were trained compared determine optimal model. The results showed that mean squared error (MSE) determination coefficient (R<sup>2</sup>) CNN validation set 0.01258/0.80, while those ANN model 0.28951/0.46. After obtaining prediction model, was used experimental specimens. values obtained experiments FEM simulation with for samples smaller than 9.5%, both accuracy efficiency surpassed simulation.</div>

Язык: Английский

Процитировано

0

Study on the Influence of Injection Velocity on the Evolution of Hole Defects in Die-Cast Aluminum Alloy DOI Open Access

Hanxue Cao,

Qiang Zhang,

Wenna Zhu

и другие.

Materials, Год журнала: 2024, Номер 17(20), С. 4990 - 4990

Опубликована: Окт. 12, 2024

Aluminum alloy die casting has achieved rapid development in recent years and been widely used all walks of life. However, due to its high pressure high-speed technological characteristics, avoiding hole defects become a problem great significance aluminum production. In this paper, the filling visualization dynamic characterization experiment was innovatively developed, which can directly study analyze influence different injection rates on formation evolution flow patterns gas-induced defects. As speed increased from 1.0 m/s 1.5 m/s, average porosity 7.49% 9.57%, marking an increase number size pores. According comparison with Anycasting, simulation results show that liquid metal when front vs. previous rate caused fractures at same distance. Therefore, degree broken splash is more serious. Combined analysis transport mechanics, fracturing wall-attached jet effect process. It difficult for adhere type wall order fuse subsequent form cavity With velocity, microgroup volume formed via breakage decreases; thus air entrapment increases, finally leading

Язык: Английский

Процитировано

1