Dinic’s assembly parts matching optimization based on CACGAN gyroscope performance prediction

In dynamic servo gyroscope assembly, gyroscope operational performance is critically governed by gyro framework-solenoid component compatibility, as inherent assembly and manufacturing variations induce non-negligible system-level uncertainties. Performance overproof arising from random component pairing causes parts re-assembly and compromises production efficiency, for which optimal parts matching strategies is required. Effective parts matching optimization requires precise gyroscope performance prediction, while conventional methods prove inadequate under the data-scarce manufacturing scenarios. To address these challenges, first, a Dinic’s network flow optimization architecture is proposed to optimize gyroscope parts matching performance, minimizing assembly repetition in gyroscope production. Second, a Cross-pretraining Auxiliary Classifier GAN gyroscope performance prediction framework is introduced to provide parts matching optimization weights, by leveraging assembly parameter associations under small-sample conditions. Third, model structural optimizations with residual connection are incorporated to improve the stability and robustness in adversarial training under noisy and variated assembly data scenarios. Experiments demonstrate that the proposed framework achieves high-precision parts matching performance prediction and optimal gyroscope parts matching, outperforming all other existing methods. The parts matching optimization method reduces theoretical assembly repetition rate to only 5.32% and significantly improves dynamic servo gyroscope assembly efficiency.