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作 者:魏新园 周京欢 张楠 李丹 顾浩然 Wei Xinyuan;Zhou Jinghuan;Zhang Nan;Li Dan;Gu Haoran(School of Electrical and Information Engineering,Anhui University of Technology,Ma′anshan 243032,China;Anhui Intelligent Demolition Equipment Engineering Laboratory,Ma′anshan 243032,China)
机构地区:[1]安徽工业大学电气与信息工程学院,马鞍山243032 [2]安徽省智能破拆装备工程实验室,马鞍山243032
出 处:《仪器仪表学报》2024年第12期256-263,共8页Chinese Journal of Scientific Instrument
基 金:安徽省重点研究与开发计划(2022f04020005);安徽省高等学校科研研究重点(2022AH050313);安徽省智能破拆装备工程实验室开放基金(APELIDE2023A008)项目资助。
摘 要:数据驱动的超声缺陷识别在航空航天和工业制造等领域中应用广泛,但大量实验数据难于获取。软件仿真数据虽易于获取,但与实验数据存在差异,直接应用效果并不理想。对此,提出基于深度迁移学习的超声缺陷识别方法研究。首先同时对试件中的不同形状、尺寸和深度的缺陷进行超声检测实验和超声检测仿真,得到实验数据和仿真数据。进而基于仿真数据建立超声缺陷识别深度学习模型,然后基于少量实验数据利用深度迁移学习方法对所建模型进行迁移学习,从而建立能够对实验数据实现准确缺陷识别的模型,最后对所建模型的预测效果进行实验验证。结果表明,基于仿真数据建立的超声损伤识别模型经过迁移后,缺陷识别准确率和精确率大幅提升,均达到0.956。Data-driven ultrasonic defect identification is widely used in aerospace and industrial manufacturing.However,obtaining a large amount of experimental data remains challenging.While software-generated simulation data is easier to acquire,it differs significantly from experimental data,leading to suboptimal performance when applied directly.In this paper,the ultrasonic defect recognition method based on deep transfer learning is proposed.Firstly,ultrasonic testing experiments and ultrasonic testing simulations were carried out for defects of different shapes,sizes and depths in the specimen at the same time,generating both experimental and simulation data.Furthermore,a deep learning model for ultrasonic defect recognition was established based on simulation data.Then,a small amount of experimental data was employed to fine-tune the pre-trained model through transfer learning.So as to established a model that can achieve accurate defect recognition on the experimental data.Finally,the prediction effect of the built model was verified through experiments..The results show that after transfer learning,the accuracy and precision of the ultrasonic defect recognition model significantly improved,both achieving a value of 0.956.Data-driven ultrasonic defect identification is widely used in aerospace and industrial manufacturing,but it is difficult to obtain a large number of experimental data.Although the software simulation data is easy to obtain,it is different from the experimental data,and the direct application effect is not ideal.In this paper,the ultrasonic defect recognition method based on deep transfer learning is proposed.Firstly,ultrasonic testing experiment and ultrasonic testing simulation were carried out for defects of different shapes,sizes and depths in the specimen at the same time,and experimental data and simulation data were obtained.Furthermore,a deep learning model for ultrasonic defect recognition is established based on simulation data.Then a small amount of experimental data is used to carry ou
分 类 号:TH164[机械工程—机械制造及自动化] TN05[电子电信—物理电子学] TG115.28[金属学及工艺—物理冶金]
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