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

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.

Automated summary

This paper presents a new image recognition system based on a deep convolutional neural network for quality monitoring of L-DED processed parts. The system utilizes lightweight architectures to reduce detection time and improves accuracy through the enhancement of the penalty function.