Defect Identification Method for Laser Melting Deposition Process

被引：0

作者：

Liu, Wei-Wei ^{[1
,2
]}

Liu, Bing-Jun ^{[1
]}

Liu, Huan-Qiang ^{[1
]}

Liu, Ze-Yuan ^{[1
]}

机构：

[1] School of Mechanical Engineering, Dalian University of Technology, Dalian

[2] State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2024年 / 45卷 / 08期

关键词：

lack of fusion; laser melting deposition; long‑term recurrent convolutional neural network（LRCN）; molten pool transient characteristics; residual neural network（ResNet）;

D O I：

10.12068/j.issn.1005-3026.2024.08.011

中图分类号：

学科分类号：

摘要：

Defects in laser melting deposition are key problems restricting its development. Achieving precise automatic identification of defects is a crucial approach to enhance the application level of laser melting deposition technology. A novel algorithm for extracting the melt pool’s transient characteristics was presented， and the relationship between transient characteristics and lack of fusion defects of the deposition layers was found. Moreover，a dataset of the melt pool’s transient characteristics was established. The mainstream recognition algorithms were trained and tested，leading to the identification of the most effective model，ResNet 34. In order to solve the poor fitting training loss effect and slow calculating speed of ResNet 34，a hybrid LRCN 64 model was proposed combining the traditional convolutional networks and LSTM（long short‑term memory）networks. It exhibited remarkable accuracy and significant calculating speed. The testing accuracy of the LRCN 64 model reaches 95. 8%，thereby realizing the identification of lack of fusion defects，which provides valuable technical support to facilitate online non‑destructive testing of deposited parts. © 2024 Northeast University. All rights reserved.

引用

页码：1150 / 1158

页数：8

共 20 条

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