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Your search keyword '"Tang, Tianxiang"' showing total 17 results
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17 results on '"Tang, Tianxiang"'

7. Improved Analytical Model for Thermal Softening in Aluminum Alloys Form Room Temperature to Solidus.

Author

Chen, Gaoqiang, Liu, Xin, Qiao, Junnan, Tang, Tianxiang, Zhang, Hua, Xing, Songling, Zhang, Gong, and Shi, Qingyu

Subjects
ALUMINUM forming, FRICTION stir welding, FRICTION stir processing, STRENGTH of materials, MANUFACTURING processes
Abstract

In advanced solid-state manufacturing processes such as friction stir welding, the metal's temperature ranges from room temperature to the solidus temperature. The material strength in the temperature range is generally required for investigating the mechanical behaviors. In this communication paper, an analytical model is proposed for describing the thermal softening of aluminum alloys for room temperature to solidus temperature, in which the concept of temperature-dependent transition between two thermal softening regimes is implemented. It is demonstrated that the proposed model compares favorably to the well-known Sellars–Tegart model and Johnson–Cook model. The constants of the proposed model for nine typical engineering commercial aluminum alloys are documented. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

Author

Zhang, Chunbo, Shi, Qingyu, Wang, Yihe, Qiao, Junnan, Tang, Tianxiang, Zhou, Jun, Liang, Wu, Chen, Gaoqiang, Zhang, Chunbo, Shi, Qingyu, Wang, Yihe, Qiao, Junnan, Tang, Tianxiang, Zhou, Jun, Liang, Wu, and Chen, Gaoqiang

Abstract

Artificial neural networks (ANNs) have been an important approach for predicting the value of flow stress, which is dependent on temperature, strain, and strain rate. However, there is still a lack of sufficient knowledge regarding what structure of ANN should be used for predicting metal flow stress. In this paper, we train an ANN for predicting flow stress of In718 alloys at high temperatures using our experimental data, and the structure of the ANN is optimized by comparing the performance of four ANNs in predicting the flow stress of In718 alloy. It is found that, as the size of the ANN increases, the ability of the ANN to retrieve the flow stress results from a training dataset is significantly enhanced; however, the ability to predict the flow stress results absent from the training does not monotonically increase with the size of the ANN. It is concluded that the ANN with one hidden layer and four nodes possesses optimized performance for predicting the flow stress of In718 alloys in this study. The reason why there exists an optimized ANN size is discussed. When the ANN size is less than the optimized size, the prediction, especially the strain dependency, falls into underfitting and fails to predict the curve. When the ANN size is less than the optimized size, the predicted flow stress curves with the temperature, strain, and strain rate will contain non-physical fluctuations, thus reducing their prediction accuracy of extrapolation. For metals similar to the In718 alloy, ANNs with very few nodes in the hidden layer are preferred rather than the large ANNs with tens or hundreds of nodes in the hidden layers.

Published
2023

10. Transcription factor 4 controls positioning of cortical projection neurons through regulation of cell adhesion

Author

Zhang, Yandong, Cai, Zheping, Hu, Guanglei, Hu, Songhui, Wang, Yafei, Li, Na, Chen, Saiyong, Liu, Qiong, Zeng, Lanhui, Tang, Tianxiang, Zhang, Yilan, Xiao, Lei, Gu, Yu, and Xie, Yunli

Abstract

The establishment of neural circuits depends on precise neuronal positioning in the cortex, which occurs via a tightly coordinated process of neuronal differentiation, migration, and terminal localization. Deficits in this process have been implicated in several psychiatric disorders. Here, we show that the transcription factor Tcf4 controls neuronal positioning during brain development. Tcf4-deficient neurons become mispositioned in clusters when their migration to the cortical plate is complete. We reveal that Tcf4 regulates the expression of cell adhesion molecules to control neuronal positioning. Furthermore, through in vivo extracellular electrophysiology, we show that neuronal functions are disrupted after the loss of Tcf4. TCF4 mutations are strongly associated with schizophrenia and cause Pitt-Hopkins syndrome, which is characterized by severe intellectual disability. Thus, our results not only reveal the importance of neuronal positioning in brain development but also provide new insights into the potential mechanisms underlying neurological defects linked to TCF4 mutations.

Published
2024
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11. TEP1 is a risk gene for sporadic cerebral palsy

Author

Wang, Yangong, primary, Qiao, Yimeng, additional, Cheng, Ye, additional, Su, Yu, additional, Song, Lili, additional, Xu, Yiran, additional, Li, Hongwei, additional, Zhang, Lingling, additional, Song, Juan, additional, Zhang, Xiaoli, additional, Wang, Jun, additional, Zhu, Dengna, additional, Tang, Tianxiang, additional, Shang, Qing, additional, Gao, Chao, additional, Wang, Xiaoyang, additional, Zhu, Changlian, additional, and Xing, Qinghe, additional

Published
2021
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17. TEP1is a risk gene for sporadic cerebral palsy

Author

Wang, Yangong, Qiao, Yimeng, Cheng, Ye, Su, Yu, Song, Lili, Xu, Yiran, Li, Hongwei, Zhang, Lingling, Song, Juan, Zhang, Xiaoli, Wang, Jun, Zhu, Dengna, Tang, Tianxiang, Shang, Qing, Gao, Chao, Wang, Xiaoyang, Zhu, Changlian, and Xing, Qinghe

Published
2021
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