Method for detecting defects during laser additive manufacturing, data processing device, computer program and storage medium for implementing the method

A method for detecting defects during laser additive manufacturing, comprising the following steps:

• B21) a first image is captured, said first image being the image, captured in an infrared spectrum, of an upper surface of a layer of powder exposed to laser scan;
• B23) said first image is processed using a first convolutional neural network of self-encoding type, in such a way as to produce a defect mask indicating a location of defects at the upper surface of said layer of powder;
• wherein during the capturing step B21, a second image of the upper surface exposed to laser radiation is also captured and a third image of the upper surface exposed to the laser scan is also captured, the third image is an image of said surface, captured in a visible spectrum;
• the second image is an image of said surface, captured in the infrared spectrum, wherein for each pixel, an intensity of the pixel represents a maximum luminous energy received by the pixel during a capturing period;
• during the processing step B23, the processing carried out takes the second image and the third image into account as input, in addition to the first image;
• and
• during the processing step B23, the processing is carried out using a set of neural networks comprising:
• as input, said first convolutional neural network, and at least one first additional neural network of self-encoding type configured to receive as input a first additional image from among said second image and said third image;
• and
• as output, an output neural network of self-encoding type configured to receive as input the outputs of the first neural network and of said at least one first additional neural network, and to produce as output said defect mask.