Fast prediction method of thermal conductivity based on phenolic resin scanning electron microscope image

By constructing a three-dimensional model and a machine learning surrogate model of phenolic resin-based composite materials and extracting structural parameters using scanning electron microscopy images, the problems of speed and accuracy in evaluating the thermal conductivity of phenolic resin-based composite materials in existing technologies have been solved, and the prediction of thermal conductivity of small-sized samples and complex-shaped specimens has been realized.

CN122368320APending Publication Date: 2026-07-10BEIJING INST OF TECH
-1 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING INST OF TECH
Filing Date
2026-04-13
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies struggle to quickly and accurately assess the thermal conductivity of phenolic resin-based composites, especially in small-sized specimens and complex-shaped pieces. Furthermore, existing methods neglect the influence of nanoscale pores and particle structures, leading to significant discrepancies between calculated results and actual thermal conductivity.

Method used

By constructing a three-dimensional model of phenolic resin-based composite materials, extracting structural parameters using conventional scanning electron microscopy images, and combining this with a machine learning surrogate model, rapid prediction of thermal conductivity is achieved. Specific steps include constructing a sample set, image segmentation, skeleton extraction, and structural parameter calculation, which are then input into a trained regression model for thermal conductivity prediction.

Benefits of technology

It enables rapid and accurate prediction of the thermal conductivity of phenolic resin-based composite materials, applicable to small-sized samples and complex-shaped specimens, reducing sample preparation and calculation costs, and improving evaluation efficiency and accuracy.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122368320A_ABST
    Figure CN122368320A_ABST
Patent Text Reader

Abstract

This invention discloses a rapid prediction method for the thermal conductivity of phenolic resin based on scanning electron microscopy (SEM) images. This invention requires only conventional SEM images, extracts structural parameters such as porosity, equivalent particle diameter, and bond ratio through automated image processing, and then inputs these structural parameters into a pre-trained machine learning surrogate model to achieve rapid prediction of the material's thermal conductivity. Compared with existing methods that require multi-scale 3D reconstruction and direct numerical solutions, this invention significantly reduces the requirements for sample size and characterization equipment, simplifies the modeling process, and achieves high computational efficiency. Furthermore, because the prediction model fully incorporates the influence of nanoscale pores and particle structure on thermal conductivity during the training phase, this invention can quickly provide the effective thermal conductivity of resin-based composite materials while maintaining high accuracy, providing an efficient and reliable means of evaluating thermal conductivity performance for material formulation design and process optimization.
Need to check novelty before this filing date? Find Prior Art