Application of Artificial Intelligence in Material Testing

Authors

  • Tianyi Shi
  • Wei Huang
  • Man Zhang
  • Jingyue Wu

DOI:

https://doi.org/10.54097/hset.v1i.445

Keywords:

machine learning, machine vision, Material Testing

Abstract

Under the development of artificial intelligence technology, the technical level of machine learning and machine vision has been significantly improved. For testing, machine vision can input the characteristics of the inspected object into the computer, while machine learning ability enables the computer to better analyze the characteristics of the inspected object and make the testing conclusion. Compared with traditional testing methods, this process has the characteristics of high accuracy and high speed, and its excellent performance can be used in all aspects of material testing.

Downloads

Download data is not yet available.

References

Wang Xiaoxu Review of research on inspection and testing industry [J] National circulation economy, 2021 (2) DOI: 10.3969/j.issn. 1009-5292. 2021.02.011.

Li Guojie, Cheng Xueqi Big data research: a major strategic field of future scientific, technological, economic and social development -- Research Status and scientific thinking of big data [J] Journal of the Chinese Academy of Sciences, 2012,27 (6): 647-657 DOI: 10.3969/j.issn. 1000-3045.2012.06.001.

Chris Anderson. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, Wired, 2008, 16(7).

Xu Chao Application analysis of artificial intelligence technology in special equipment inspection [J] China Equipment Engineering, 2020 (7) DOI: 10.3969/j.issn. 1671-0711.2020.07.097.

Zhang Weiguang, Zhong Jingtao, Yu Jianxin, et al Research on pavement crack detection technology based on machine learning and image processing [J] Journal of Central South University (NATURAL SCIENCE EDITION), 2021 (7) DOI: 10.11817/j.issn. 1672-7207.2021.07.026.

Tan Wenqi, Wang Ruiyao, Wang Jingchao, et al Research on on-line burr detection based on machine learning [J] Integrated circuit applications, 2021 (9) DOI: 10.19339/j.issn. 1674-2583.2021.09.116.

Goofy Research and implementation of road comprehensive information acquisition system [D] Xi'an: Chang'an University, 2009:2 − 5

Guan Rizhao, Wu Lei, Xu Zhuoji Defect detection of transparent plastic parts based on machine vision [J] Equipment manufacturing technology, 2018 (3) DOI: 10.3969/j. issn. 1672-545X. 2018.03.053.

Fu Guangwei, Zhang Zhenzhu, Li Hongying, et al Application of artificial intelligence in textile testing [J] Textile report, 2021 (2)

Wei long, Liu Le, Liu Jiji, et al Intelligent interpretation of nondestructive testing data of solid rocket motor based on machine learning [J] National Defense Science and technology, 2021 (4) DOI:10.13943/j. issn1671-4547.2021.04.12.

Xu Sai, Lu Huazhong, Zhou Zhiyan, et al. Orchard litchi maturity recognition method based on physical and chemical indexes and electronic nose [J / 0l]. Journal of agricultural machinery, 2015, 46 (12): 226-232

Li Xinxing, Dong Baoping, Yang Mingsong, et al Salmon freshness detection system based on SVM kernel machine learning [J] Journal of agricultural machinery, 2019 (5) DOI: 10.6041/j. issn. 1000-1298.2019.05.043.

To Venus Application analysis of artificial intelligence technology in special equipment inspection [J] Construction engineering technology and design, 2020 (20) DOI: 10.12159/j. issn. 2095-6630. 2020. 20. 0445.

Downloads

Published

14-06-2022