|Table of Contents|

Welded Surface Morphology Modeling and PredictionBased on BP Neural Network(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2021年01期
Page:
1-7
Research Field:
控制科学与工程
Publishing date:

Info

Title:
Welded Surface Morphology Modeling and PredictionBased on BP Neural Network
Author(s):
Huang Wuyun1; Liu Yijian1; Liu Zongxi1; Yang Jiquan1; Zhu ZhaoWei1; Xie Fei1; Shi Jianjun2
(1.NARI School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China)(2.Nanjing Zhongke Yuchen Laser Technology Co.,Ltd.,Nanjing 210038,China)
Keywords:
weld bead morphology; BP neural network; optimization solution; PSO algorithm
PACS:
TP183
DOI:
10.3969/j.issn.1672-1292.2021.01.001
Abstract:
A good weld bead provides a guarantee for successful wire arc additive manufacturing,which is affected by various parameters such as welding current,voltage,scanning speed,and wire feed speed. A four-input and two-output BP neural network model is proposed,which is applied to the surface morphology characteristic identification of the weld bead height and width. The particle swarm optimization(PSO)algorithm is used to optimize the neural network weight. Experimental results show that the BP neural network designed in this paper realizes the prediction of the weld bead morphology and that it provides a model basis for the real-time prediction and control of subsequent arc additive manufacturing.

References:

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Last Update: 2021-03-15