دورية أكاديمية
An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN)
العنوان: | An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN) |
---|---|
المؤلفون: | Zhongman Cai, Hongchao Ji, Weichi Pei, Xuefeng Tang, Long Xin, Yonghao Lu, Wangda Li |
المصدر: | Materials, Vol 13, Iss 6, p 1282 (2020) |
بيانات النشر: | MDPI AG, 2020. |
سنة النشر: | 2020 |
المجموعة: | LCC:Technology LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Engineering (General). Civil engineering (General) LCC:Microscopy LCC:Descriptive and experimental mechanics |
مصطلحات موضوعية: | artificial neural network, austenitic stainless steel, dynamic recrystallization, grain size, processing map, sensitivity analysis, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85 |
الوصف: | Based on an 33Cr23Ni8Mn3N thermal simulation experiment, the application of an artificial neural network (ANN) in thermomechanical processing was studied. Based on the experimental data, a microstructure evolution model and constitutive equation of 33Cr23Ni8Mn3N heat-resistant steel were established. Stress, dynamic recrystallization (DRX) fraction, and DRX grain size were predicted. These models were evaluated by a variety of statistical indicators to determine that these models would work well if applied in predicting microstructure evolution and that they have high precision. Then, based on the weight of the ANN model, the sensitivity of the input parameters was analyzed to achieve an optimized ANN model. Based on the most widely used sensitivity analysis (SA) method (the Garson method), the input parameters were analyzed. The results show that the most important factor for the microstructure of 33Cr23Ni8Mn3N is the strain rate ( ε ˙ ). For the control of the microstructure, the control of the ε ˙ is preferred. ANN was applied to the development of processing map. The feasibility of the ANN processing map on austenitic heat-resistant steel was verified by experiments. The results show that the ANN processing map is basically consistent with processing map based on experimental data. The trained ANN model was implanted into finite element simulation software and tested. The test results show that the ANN model can accurately expand the data volume to achieve high precision simulation results. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1996-1944 |
Relation: | https://www.mdpi.com/1996-1944/13/6/1282; https://doaj.org/toc/1996-1944 |
DOI: | 10.3390/ma13061282 |
URL الوصول: | https://doaj.org/article/785d42dd48cf486182001da79e6741d8 |
رقم الأكسشن: | edsdoj.785d42dd48cf486182001da79e6741d8 |
قاعدة البيانات: | Directory of Open Access Journals |
كن أول من يترك تعليقا!