The purpose of this paper is to achieve fast densification prediction of target materials and particularly of difficult-to-sintering materials in small dataset sintering scenarios. A multi-source sintering transfer learning framework based on the domain adversarial network (DANN) was proposed to achieve multi-source sintering transfer learning. Further, a calibration method was presented to enhance the reliability of the integrated multi-source DANN (IMDANN), where DANN was applied as a test benchmark. The results indicate that IMDANN is significantly better than the test benchmark under all the test conditions. Error analysis illustrates that the root mean square error (RMSE) of IMDANN’s prediction converges to approximately 5% in the target domain, and the average prediction error is reduced by 56.5%. With an increasing number of source domains following the correlation criterion, the minimum number of source domains required for convergence is only 3–4. Compared to DANN, the calibrated IMDANN has high reliability and realises the cross-domain transfer of sintering knowledge with a small dataset.

本文的目的是在小数据集烧结场景中实现对目标材料，特别是难烧结材料的快速致密化预测。提出了一种基于域对抗网络（DANN）的多源烧结迁移学习框架来实现多源烧结迁移学习。此外，提出了一种校准方法来提高集成多源 DANN (IMDANN) 的可靠性，其中 DANN 被用作测试基准。结果表明，IMDANN 在所有测试条件下都明显优于测试基准。误差分析表明，IMDANN预测的均方根误差（RMSE）在目标域收敛到约5%，平均预测误差降低了56.5%。随着遵循相关标准的源域数量不断增加，收敛所需的最小源域数量仅为 3-4。与DANN相比，标定后的IMDANN具有较高的可靠性，可以用小数据集实现烧结知识的跨域转移。