The invention discloses a warping prediction optimization method for a complex
carbon fiber composite suspension structure, and the method comprises the steps: S1, reading CAD and laying
layers, and tracking a main
heat flow along fibers to segment an anisotropic block; s2, rearranging nodes, locally fitting a principal curvature and a
control area, extracting characteristics of a
fiber angle, a
layer thickness, a temperature and warping, and constructing a weighted adjacency graph; s3, retrieving a historical high-fidelity sample, mapping and enhancing the historical high-fidelity sample into a source domain graph, and performing confrontation training by combining an
encoder and
a domain discriminator to realize node-level alignment; s4, constructing a generator and a
discriminator based on the alignment graph, and carrying out alternate confrontation optimization after preheating; s5, constructing reconstruction, confrontation, peak stress and
energy constraint multi-target loss; and S6, the freezing generator reasones initial version warping, and evaluates a node risk clustering high-risk sub-domain. According to the method, on the premise that node-level physical conservation is guaranteed, the warping morphology of the
carbon fiber composite component with any shape and
fiber laying layer combination under the curing working condition can be rapidly and accurately predicted.