Metal additive forming fusion depth real-time prediction method based on depth and transfer learning

Abstract

The invention discloses a laser metal additive manufacturing fusion depth prediction system based on deep learning and transfer learning, which comprises a printing workbench, an image acquisition device, a thermal imager, a man-machine interaction device, a display and a host; the image acquisition device, the thermal imager, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters of the picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the system has the advantage that the training efficiency of the deep learning convolutional neural network modelis improved; and the fusion depth is predicted by adopting a deep learning convolutional neural network model, so that the precision of parameters can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of additive manufacturing, and in particular relates to a real-time prediction method for metal additive forming penetration depth based on depth and transfer learning. Background technique [0002] Single-pass forming dimensions, such as forming width, forming height, and penetration depth, are important factors affecting the quality of additive manufacturing, and the characteristics of the molten pool are the most direct factors affecting the forming quality. Therefore, it is of great significance to study the changes of molten pool characteristics in the process of additive manufacturing and realize the control of certain parameters of the molten pool to ensure the quality of additive manufacturing. An important part of intelligence. In recent years, with the development of computer vision technology, using machine vision to directly observe the molten pool of additive manufacturing, obtain the geometric...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the problems faced in the prior art, for example: there is interference in the collected image data, and it is difficult to process accurately; The resulting predicted value has large errors and low precision; when the data samples are difficult to obtain or the number of data samples is small, the prediction results of the established deep learning model are inaccurate

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