Chen Xuewen, Fan Weizhong
Guangdong Engineering Polytechnic College, Guangzhou, China.
Huajin New Materials Research Institute (Guangzhou) Co., Ltd., Guangzhou, China.
Front Neurorobot. 2022 Sep 15;16:1000646. doi: 10.3389/fnbot.2022.1000646. eCollection 2022.
The properties of metallic materials have been extensively studied, and nowadays the tensile properties testing techniques of metallic materials still have not found a suitable research method. In this paper, the neural Turing machine model is first applied to explore the tensile properties of metallic materials and its usability is demonstrated. Then the neural Turing machine model was improved. The model is then improved so that the required results can be obtained faster and more explicitly. Based on the improved Neural Turing Machine model in the exploration of tensile properties of metal materials, it was found that both H-NTM and AH-NTM have less training time than NTM. A-NTM takes more training time than AH-NTM. The improvement reduces the training time of the model. In replication, addition, and multiplication, the training time is reduced by 6.0, 8.8, and 7.3%, respectively. When the indentation interval is 0.5-0.7 mm, the error of the initial indentation data is large. The error of the tensile properties of the material obtained after removing the data at this time is significantly reduced. When the indentation interval is 0.8-1.5 mm, the stress is closer to the real value of tensile test yield strength 219.9 Mpa and tensile test tensile strength 258.8 Mpa. this paper will improve the neural Turing machine model in the exploration of metal material tensile properties testing technology has some application value.
金属材料的性能已得到广泛研究，而如今金属材料的拉伸性能测试技术仍未找到合适的研究方法。本文首次将神经图灵机模型应用于探索金属材料的拉伸性能，并证明了其可用性。然后对神经图灵机模型进行了改进。接着对该模型进行改进，以便能更快、更明确地获得所需结果。基于改进后的神经图灵机模型在金属材料拉伸性能探索中发现，H-NTM和AH-NTM的训练时间均比NTM少。A-NTM的训练时间比AH-NTM多。这种改进减少了模型的训练时间。在复制、加法和乘法运算中，训练时间分别减少了6.0%、8.8%和7.3%。当压痕间距为0.5 - 0.7毫米时，初始压痕数据的误差较大。此时去除该数据后得到的材料拉伸性能误差显著降低。当压痕间距为0.8 - 1.5毫米时，应力更接近拉伸试验屈服强度219.9兆帕和拉伸试验抗拉强度258.8兆帕的真实值。本文对神经图灵机模型在金属材料拉伸性能测试技术探索中的改进具有一定应用价值。
