李甲, 方棋洪. 人工智能结合大数据技术在材料拉伸性能教学中的应用1)[J]. 力学与实践, 2022, 44(1): 212-217. DOI: 10.6052/1000-0879-21-152
引用本文: 李甲, 方棋洪. 人工智能结合大数据技术在材料拉伸性能教学中的应用1)[J]. 力学与实践, 2022, 44(1): 212-217. DOI: 10.6052/1000-0879-21-152
LI Jia, FANG Qihong. APPLICATION OF ARTIFICIAL INTELLIGENCE COMBINED WITH BIG DATA TECHNOLOGY IN THE TEACHING OF MATERIAL TENSILE PROPERTIES1)[J]. MECHANICS IN ENGINEERING, 2022, 44(1): 212-217. DOI: 10.6052/1000-0879-21-152
Citation: LI Jia, FANG Qihong. APPLICATION OF ARTIFICIAL INTELLIGENCE COMBINED WITH BIG DATA TECHNOLOGY IN THE TEACHING OF MATERIAL TENSILE PROPERTIES1)[J]. MECHANICS IN ENGINEERING, 2022, 44(1): 212-217. DOI: 10.6052/1000-0879-21-152

人工智能结合大数据技术在材料拉伸性能教学中的应用1)

APPLICATION OF ARTIFICIAL INTELLIGENCE COMBINED WITH BIG DATA TECHNOLOGY IN THE TEACHING OF MATERIAL TENSILE PROPERTIES1)

  • 摘要: 针对材料拉伸试验获得应力-应变曲线随机性及不确定性问题,开展了人工智能结合大数据技术确定材料弹性模量、屈服强度和极限强度的教学方法。通过读取拉伸数据并构建数据库,运用概率统计理论建立合理的应力-应变曲线,并结合人工智能评估现有试验结果准确性及失效原因。人工智能与大数据技术辅助的创新教学方法,将成为材料力学教学重要的发展方向,并培养学生通过大数据视角理解材料变形不确定性的能力和科研兴趣。

     

    Abstract: In view of the randomness and uncertainty of the stress-strain curves obtained from the material tensile experiments, a new teaching method for determining the elastic modulus, yield strength, and ultimate strength of materials is developed, which is based on the artificial intelligence combined with the big data technology. Through compiling the database of tensile test data, the reasonable stress-strain curve is established using the probability and statistics theory. The accuracy of the existing experimental results is evaluated using the machine learning method, and the possible failure reasons are given using the artificial intelligence. The innovative teaching method assisted by the artificial intelligence and big data technology would become an important development direction of the teaching methods in mechanics of materials. More importantly, this teaching method would cultivate the ability and interest of undergraduates in scientific research to understand the uncertainty of the material deformation from the perspective of the big data.

     

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