Workpiece surface morphology generation method and apparatus based on multimodal image generation

By adopting diffusion models in the field of machining, the accurate mapping of workpiece multimodal information to surface morphology images is solved, and the problem of complex and time-consuming surface morphology simulation methods and insufficient generation and adversarial network accuracy in the prior art is solved, and fast and high-fidelity surface morphology image generation and real-time prediction are achieved.

WO2025060317A1 Publication Date: 2025-03-27ZHEJIANG LAB
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Patent Information

Application Number
PCT/CN2024/073663
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2023-09-22
Filing Date
2024-01-23
Publication Date
2025-03-27

AI Technical Summary

Technical Problem

In the field of machining, the surface morphology simulation method based on analytical modeling has problems such as complex modeling, large calculation and long time. The method based on the generation of adversarial networks is insufficient accuracy and poor generalization performance.

Method used

Using the diffusion model, from the multimodal information of the workpiece to the accurate mapping of the surface morphology image, the variational autoencoder composed of the encoder and the decoder is trained and generated using the forward diffusion and reverse diffusion processes to achieve fast and high-fidelity surface morphology grayscale image generation.

Benefits of technology

It greatly reduces the data acquisition time and image generation time, avoids the problem of pattern crash and poor generalization performance of the generative adversarial network during training, and can generate high-quality, diverse and realistic surface morphology images, and is suitable for surface morphology prediction in real-time processing scenarios.

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Abstract

Disclosed in the present invention are a workpiece surface morphology generation method and apparatus based on multimodal image generation, belonging to the technical field of machining data processing. The method comprises: on the basis of multimodal information of historical data of different machining modes, constructing a guide vector; on the basis of a diffusion model, adding noise to a low-dimensional representation of a surface morphology grayscale image to obtain a noise vector, inputting the guide vector, a time step length, and the noise vector into a reverse diffusion process, and reducing noise layer by layer to obtain another low-dimensional representation, thereby training the model; extracting target multimodal information to construct a target guide vector, inputting a random noise hidden variable and the target guide vector into the trained diffusion model to obtain a target low-dimensional representation, and obtaining a target surface morphology grayscale image by means of a decoder; and using an image quality comprehensive evaluation module to perform quality evaluation. The present invention uses a diffusion model to achieve accurate mapping of multimodal information to a surface morphology image, having the characteristics of rapid generation and high fidelity, and having great potential for real-time surface morphology prediction.
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