Testing technology for tensile properties of metal materials based on deep learning model - Chen, Xuewen; Fan, Weizhong (Frontiers in Neurorobotics, 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 0.5mm-0.7mm, 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 0.8mm-1.5mm, the stress is closer to the real value of tensile test yield strength 219.9Mpa and tensile test tensile strength 258.8Mpa. this paper will improve the neural Turing machine model in the exploration of metal material tensile properties testing technology has some application value.