Weijun Liu

Weijun Liu


Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 March 2020 in Applied Sciences
Verified
Journal Article
Open access
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AJAnyuan Jiao
Anyuan Jiao
GZGuofu Zhang
Guofu Zhang
BLBinghong Liu
Binghong Liu
WLWeijun Liu
Weijun Liu
In order to improve the manufacturing quality of holes (Φ3–Φ8 mm) and to optimize the hole drilling process in T300 carbon fiber-reinforced plastic (CFRP) and 7050-T7 Al alloy stacks, a prediction model of multiple objective parameter optimization was proposed based on a back propagation (BP) neural network algorithm. Four parameters of feed rate, spindle speed, drilling diameter, and cushion plate were taken as the input layer parameters to study the manufacturing quality of holes in four stack types: CFRP/Al, Al/CFRP, Al/CFRP/Al, and CFRP/Al/CFRP. Delamination and tearing defects often appear in the drilling process; thus, a certain degree of defects in CFRP was selected as the output parameter, in an effort to build a prediction model of drilling quality. After the neural network model of the optimized hole-making process of an 8–14–1 three-layer topology was corrected by 170 steps, the error was reduced to 0.00016882, the regression fitting was 0.99978, and the fitting error of training samples was 10−2~10−5. The prediction model of the number of defective holes provided basically similar results to the experimental data. This indicates that the prediction model based on a BP neural network has good prediction ability. Based on the prediction of parameters, verification tests were performed, and the number of defective holes in CFRP was reduced while the manufacturing quality of the holes was improved significantly; the qualified rate of manufactured holes reached 97%.

6 February 2024 in Welding in the World
Verified
Journal Article
Closed access
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WWWei WANG
Wei WANG
QJQingyu Jiang
Qingyu Jiang
WLWeijun Liu
Weijun Liu
XJXiangcheng Ji
Xiangcheng Ji
FXFei Xing
Fei Xing
JWJia Wang
Jia Wang

2 August 2024 in Journal of Materials Engineering and Performance
Verified
Journal Article
Closed access
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YSYufeng Sun
Yufeng Sun
WLWeijun Liu
Weijun Liu
XYXingfu Yu
Xingfu Yu
YWYinghua Wei
Yinghua Wei
YSYong Su
Yong Su
YNYanhui Na
Yanhui Na

14 August 2019 in International Journal of Aerospace Engineering
Verified
Journal Article
Open access
PDF file
AJAnyuan Jiao
Anyuan Jiao
WLWeijun Liu
Weijun Liu
The nickel-based superalloy GH3128 with high plasticity, high long-lasting creep strength, good resistance to oxidation and stamping, and good welding performance is widely used in aircraft engine heat shields. The many holes that need to be machined on the heat shield are not only small in diameter but also dense, and GH3128 as a typical hard-to-process material has the problems of large cutting force, high cutting temperature, and serious hardening. Therefore, poor dimensional accuracy and residual burrs have become the main factors that limit the processing efficiency and processing quality. So, a novel combination of manufacturing processes was proposed. Firstly, laser cutting technology was used to process the base hole in a GH3128 plate, followed by reaming, and finally, using a magnetic abrasive finishing effector to remove burrs formed during the first two steps. The whole drilling process of the heat shields fully meets the requirements of the technical parameters. This study provides new reference for manufacturing the holes of a heat shield and other similar porous parts.