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• W4226116145 abstract "The thermal protection layer of the solid rocket motor (SRM) is an important elastic material applied to the adhesive shell and propellant; the thickness of the layer is related to the performance of the SRM. This paper proposes a method of linear laser scanning to realise the precise thickness measurement of the thermal protection layer. The custom-designed data acquisition device separately collects distance values from the inner surface of the thermal protection layer, before and after coating, using a linear laser sensor. The two measured distance values are subtracted and D-H modelling (a classical kinematics modelling method proposed by Denavit and Hartenberg) is conducted, then the system errors are analysed and calibrated to obtain the 3D point cloud data of the profile. The iterative closest point (ICP) algorithm is used to register the point cloud data relating to the internal surface, then an octree-based simplification method is proposed to realise the denoising and simplification of the point cloud data. Next, the normal vector of the point cloud data is calculated and the reference surface is extracted as the projection plane. The simplified point cloud is then projected onto the plane and the geometric information of the point cloud data after projection is extracted. A series of distance differences, namely the thickness of the thermal protection layer, is obtained. Finally, the Poisson surface reconstruction algorithm is used to display the inner surface of the thermal protection layer. The results show that the difference between the calculated values and the measured values is less than 0.1 mm, indicating that the thickness of the thermal protection layer of the SRM can be measured by the proposed non-contact method." @default.
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• W4226116145 date "2022-04-01" @default.
• W4226116145 modified "2023-09-29" @default.
• W4226116145 title "Thickness measurement method for the thermal protection layer of a solid rocket motor based on a laser point cloud" @default.
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• W4226116145 doi "https://doi.org/10.1784/insi.2022.64.4.219" @default.
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