3D printing online quality monitoring method, system and device based on neural network

Abstract

The invention belongs to the field of 3D printing, particularly relates to a 3D printing online quality monitoring method, system and device based on a neural network, and aims to solve the problems that an existing defect detection method can only detect specific defect shapes and is low in defect detection precision. The method comprises the steps of collecting an image of a three-dimensional object in the 3D printing process and using as an input image; acquiring the category of each pixel in the input image by adopting a pre-trained defect segmentation network; counting the pixel number corresponding to each type of defect in the input image, and calculating the area of the defect part in the input image by combining with the pre-acquired camera internal reference; and judging whether the area is larger than a set threshold value, if yes, starting a quality monitoring alarm, and if not, images in the 3D printing process continue to be collected. According to the method, 3D printing defects of different types and shapes can be flexibly identified, the false detection rate is reduced, and the defect detection accuracy is improved.

Description

technical field [0001] The invention belongs to the field of 3D printing, and in particular relates to a neural network-based 3D printing online quality monitoring method, system and device. Background technique [0002] 3D printing, also known as additive manufacturing and rapid prototyping, is a technology that uses CAD design data to manufacture physical parts by layer-by-layer accumulation of materials. 3D printing technology is flexible and convenient. It does not require traditional tools, fixtures, machine tools or molds. It can directly use computers to convert 3D CAD graphics into physical products, shortening the product development cycle. Compared with traditional subtractive manufacturing (cutting) technology, 3D printing has advantages in manufacturing cost, energy efficiency, and economic return in the field of personalized customization. It is used in dental and medical industries, architectural design, food industry, industrial design and Fields such as aero...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem that the existing defect detection method can only detect specific defect shapes, and the defect detection accuracy is low, the present invention proposes a 3D printing online quality monitoring based on neural network method, which includes:

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