FIELD: construction.
SUBSTANCE: invention relates to a system and method for simultaneously predicting the resistance to indentation and oil canning of car roof panels, and, in particular, to assessing the effect of placing roof strenghteners, the roof panel curvature, the roof thickness, and the steel grade on the resistance to indentation and oil canning. SUBSTANCE: performing the stages of: identifying the first major radius of curvature (R1) of the panel (12) from a metal sheet; identifying the second main radius of curvature (R2) of the panel (12); identifying the thickness (t) of the panel (12); identifying the length (L2) of the panel section between the supporting structures (32); creating the mathematical function for determining the nature of deflection under load for deflection with snapping; and determining the probability of developing the deflection with snapping of the panel (12) at various locally applied loads (26, 33) by means of entering the parameters of the main radii of curvature (R1, R2), the thickness (t) of the panel (12) and the section length (L2) of the sheet panel (12) between the supporting structures (32) together with the curve of the mathematical technique. The device comprises a means for calculating the predetermined panel (12) geometry, including at least one curvature, by the first major curvature radius (R1) of the metal sheet panel (12), the second major curvature radius (R2) of the panel (12), the thickness (t) of the panel (12), and the length (L2) of the panel section between the support structures (32); means for creating the FEA model (10) of the process of local loading (26) applied to at least one curvature (R1, R2) of the panel (12); a means for entering the geometry variables affecting the oil canning resistance of the panel (12), the curvature (R1, R2) including at least one curvature value; a means for performing virtual experiments on the panel (12), based on the entered values of the variables and the FEA model (10) of the local loading (26) process; and a means for building the regression model based on virtual experiments, wherein the output data of the regression model show the oil canning resistance of the panel (12) under the conditions of local loading (26).
EFFECT: ability to reliably evaluate alternative solutions and to obtain real-time results in minutes, compared to the time required for FEA.
20 cl, 13 dwg
