Defect Identification Method for Laser Melting Deposition Process

doi:10.12068/j.issn.1005-3026.2024.08.011

Abstract

Abstract:

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.

Key words: laser melting deposition, molten pool transient characteristics, lack of fusion, long?term recurrent convolutional neural network （LRCN）, residual neural network （ResNet）

CLC Number:

TU 621

Wei-wei LIU, Bing-jun LIU, Huan-qiang LIU, Ze-yuan LIU. Defect Identification Method for Laser Melting Deposition Process[J]. Journal of Northeastern University(Natural Science), 2024, 45(8): 1150-1158.

Figures/Tables 20

References 20

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设备名称	型号
半导体激光器	LDF 4000-100 VGP
六轴机器人	KUKA KR30HA-KRC4
激光熔覆头	PRECITEC Cladding Head YC52
送粉器	煜宸RC-PGF-D双筒载气式送粉器
冷水机	MCWL-150T-01AK1S4精密冷水机

设备名称	型号
半导体激光器	LDF 4000-100 VGP
六轴机器人	KUKA KR30HA-KRC4
激光熔覆头	PRECITEC Cladding Head YC52
送粉器	煜宸RC-PGF-D双筒载气式送粉器
冷水机	MCWL-150T-01AK1S4精密冷水机

序号	激光沉积密度	激光功率	扫描速度	z轴提升量	激光光斑直径
序号	J·mm^-2	W	mm·s^-1	mm	mm
1	40	320	4	0.5	2
2	45	360	4	0.5	2
3	50	400	4	0.5	2
4	55	440	4	0.5	2
5	60	480	4	0.5	2
6	65	520	4	0.5	2
7	70	560	4	0.5	2
8	75	600	4	0.5	2
9	43	430	5	0.5	2
10	48	480	5	0.5	2
11	53	530	5	0.5	2
12	58	580	5	0.5	2
13	63	630	5	0.5	2
14	68	680	5	0.5	2

序号	激光沉积密度	激光功率	扫描速度	z轴提升量	激光光斑直径
序号	J·mm^-2	W	mm·s^-1	mm	mm
1	40	320	4	0.5	2
2	45	360	4	0.5	2
3	50	400	4	0.5	2
4	55	440	4	0.5	2
5	60	480	4	0.5	2
6	65	520	4	0.5	2
7	70	560	4	0.5	2
8	75	600	4	0.5	2
9	43	430	5	0.5	2
10	48	480	5	0.5	2
11	53	530	5	0.5	2
12	58	580	5	0.5	2
13	63	630	5	0.5	2
14	68	680	5	0.5	2

模型	学习率	训练轮次	批数据大小	训练准确率/%	测试准确率/%
VGG 11	0.001	100	5	84.1	93.9
VGG 16	0.001	100	5	87.8	94.6
ResNet 18	0.001	100	5	88.4	96.3
ResNet 34	0.001	100	5	89.3	97.9