In-situ monitoring laser based directed energy deposition process with deep convolutional neural network

Jiqian Mi, Yikai Zhang, Hui Li (), Shengnan Shen, Yongqiang Yang, Changhui Song, Xin Zhou (), Yucong Duan, Junwen Lu and Haibo Mai
Additional contact information
Jiqian Mi: Wuhan University
Yikai Zhang: Wuhan University
Hui Li: Wuhan University
Shengnan Shen: Wuhan University
Yongqiang Yang: South China University of Technology
Changhui Song: South China University of Technology
Xin Zhou: Air Force Engineering University
Yucong Duan: Air Force Engineering University
Junwen Lu: Civil Aviation Flight University of China
Haibo Mai: Civil Aviation Flight University of China

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 2, No 16, 683-693

Abstract: Abstract Laser based directed energy deposition (L-DED) is a promising type of additive manufacturing technology. The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper introduces a new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time. In order to eliminate the interference better, it improves the penalty function, which effectively improves the accuracy. Judging from the detection results of the data set, the accuracy of the model training reaches 94.71%, which achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process. This system realizes the positioning of the spatters for the first time, and at the same time, the number of spatters and area of molten pool are correlated to the laser scanning speed and the laser power.

Keywords: Directed energy deposition; Deep learning; Molten pool; Spatter (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-021-01820-0

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