2results about How to "Low shape requirement" patented technology

This application relates to the field of conveying equipment technology, and proposes a tensioning structure and a roller motor. The tensioning structure is used to connect a driving component and a rotating component. Under the drive of the driving component, the tensioning structure can drive the rotating component to rotate around a rotation axis. The tensioning structure includes: a first connecting component, which is connected to the driving component; a limiting component, including a bottom wall and a side wall provided along the periphery of the bottom wall, with an installation groove on the side wall, and the bottom wall is detachably connected to the first connecting component so that the side wall can be sleeved on the first connecting component; and a tensioning component, which engages with the installation groove, with the side of the tensioning component opposite to the first connecting component protruding from the installation groove and abutting against the rotating component. In the above-mentioned tensioning structure, a reliable frictional force is generated between the tensioning component and the rotating component to achieve a fixed connection, thereby driving the rotating component to rotate around the rotation axis. This connection method has low requirements for the shape of the tensioning structure, reduces the requirements for processing accuracy, and solves the technical problems of existing roller and sleeve connection methods that have high processing accuracy requirements and complex disassembly and assembly.

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.