Additive manufacturing surface topography prediction method and system based on multi-source molten pool feature fusion

By fusing the melt pool feature data collected by a coaxial CMOS camera and a rangefinder near-infrared camera, combined with deep learning technology, the problem of insufficient data from a single sensor is solved, and high-precision surface morphology prediction in the laser additive manufacturing process is achieved, which is suitable for a variety of additive manufacturing processes.

CN120763684APending Publication Date: 2025-10-10ARMOR ACADEMY OF CHINESE PEOPLES LIBERATION ARMY
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Patent Information

Application Number
CN202510749634.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-06
Publication Date
2025-10-10

AI Technical Summary

Technical Problem

The data dimension of a single sensor in the existing technology is limited and cannot fully reflect the characteristics and changes of the molten pool. There is a lack of multi-source data fusion methods, and the prediction model is not accurate enough, resulting in insufficient reliability and stability in surface morphology prediction during laser additive manufacturing.

Method used

A coaxial CMOS camera and a rangefinder near-infrared camera are used to collect melt pool feature data. Combined with deep learning technology, grid mapping and adaptive multi-source model are used to achieve high-precision prediction of melt pool features and surface morphology.

Benefits of technology

It realizes real-time and accurate surface morphology prediction in the laser additive manufacturing process, improves prediction accuracy, has wide applicability, reduces costs, and is suitable for a variety of additive manufacturing processes.

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Abstract

The invention discloses an additive manufacturing surface topography prediction method and system based on multi-source molten pool feature fusion. The method comprises the steps that a visible light image and temperature field data of a molten pool in the laser additive manufacturing process are collected; key molten pool features are extracted according to the collected visible light images and temperature field data; fusing the extracted key molten pool feature data through a grid mapping method to generate a feature matrix; scanning the surface of the formed part to obtain surface topography data, and processing the surface topography data through a grid interpolation segmentation method to generate a label matrix with the same size as the feature matrix; and using a deep learning network based on an adaptive multi-source model, taking the feature matrix as an input, taking the label matrix as an output, carrying out training, and constructing a surface topography prediction model. By means of the scheme, the surface appearance in the laser additive manufacturing process is accurately predicted in real time, and an effective monitoring and predicting means is provided for quality control of additive manufacturing.
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