التفاصيل البيبلوغرافية
| العنوان: |
Multi-source sintering transfer learning in small dataset sintering prediction scenario. |
| المؤلفون: |
Zhouzhi, Wu, Xiaomin, Zhang, Zhipeng, Zhao, Hengjia, Zhang, Hongwu, Tang |
| المصدر: |
International Journal of Material Forming; Sep2021, Vol. 14 Issue 5, p1157-1170, 14p |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Material Forming is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
