Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network
Shot peening is a surface treatment process that improves the fatigue life of a material and suppresses cracks by generating residual stress on the surface. The injected small shots create a compressive residual stress layer on the material’s surface. Maximum compressive residual stress occurs at a...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Online Access: | https://www.mdpi.com/1996-1944/16/23/7406 |
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| author | Yeong-Won Choi Taek-Gyu Lee Yun-Taek Yeom Sung-Duk Kwon Hun-Hee Kim Kee-Young Lee Hak-Joon Kim Sung-Jin Song |
| author_facet | Yeong-Won Choi Taek-Gyu Lee Yun-Taek Yeom Sung-Duk Kwon Hun-Hee Kim Kee-Young Lee Hak-Joon Kim Sung-Jin Song |
| author_sort | Yeong-Won Choi |
| collection | DOAJ |
| description | Shot peening is a surface treatment process that improves the fatigue life of a material and suppresses cracks by generating residual stress on the surface. The injected small shots create a compressive residual stress layer on the material’s surface. Maximum compressive residual stress occurs at a certain depth, and tensile residual stress gradually occurs as the depth increases. This process is primarily used for nickel-based superalloy steel materials in certain environments, such as the aerospace industry and nuclear power fields. To prevent such a severe accident due to the high-temperature and high-pressure environment, evaluating the residual stress of shot-peened materials is essential in evaluating the soundness of the material. Representative methods for evaluating residual stress include perforation strain gauge analysis, X-ray diffraction (XRD), and ultrasonic testing. Among them, ultrasonic testing is a representative, non-destructive evaluation method, and residual stress can be estimated using a Rayleigh wave. Therefore, in this study, the maximum compressive residual stress value of the peened Inconel 718 specimen was predicted using a prediction convolutional neural network (CNN) based on the relationship between Rayleigh wave dispersion and stress distribution on the specimen. By analyzing the residual stress distribution in the depth direction generated in the model from various studies in the literature, 173 residual stress distributions were generated using the Gaussian function and factorial design approach. The distribution generated using the relationship was converted into 173 Rayleigh wave dispersion data to be used as a database for the CNN model. The CNN model was learned through this database, and performance was verified using validation data. The adopted Rayleigh wave dispersion and convolutional neural network procedures demonstrate the ability to predict the maximum compressive residual stress in the peened specimen. |
| first_indexed | 2024-03-09T01:47:12Z |
| format | Article |
| id | doaj.art-a732f96f5fa9434b967d3e6c78d0071c |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T01:47:12Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-a732f96f5fa9434b967d3e6c78d0071c2023-12-08T15:21:04ZengMDPI AGMaterials1996-19442023-11-011623740610.3390/ma16237406Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural NetworkYeong-Won Choi0Taek-Gyu Lee1Yun-Taek Yeom2Sung-Duk Kwon3Hun-Hee Kim4Kee-Young Lee5Hak-Joon Kim6Sung-Jin Song7School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDoosan Heavy Industries and Construction Co., Ltd., Changwon 51711, Republic of KoreaDepartment of Smart Mechanical Components and Materials, Dongyang University, Yeongju 36040, Republic of KoreaDepartment of Physics, Andong University, Andong 36729, Republic of KoreaDoosan Heavy Industries and Construction Co., Ltd., Changwon 51711, Republic of KoreaKPC Metal Co., Ltd., Gyeongsan 38412, Republic of KoreaSchool of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaSchool of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaShot peening is a surface treatment process that improves the fatigue life of a material and suppresses cracks by generating residual stress on the surface. The injected small shots create a compressive residual stress layer on the material’s surface. Maximum compressive residual stress occurs at a certain depth, and tensile residual stress gradually occurs as the depth increases. This process is primarily used for nickel-based superalloy steel materials in certain environments, such as the aerospace industry and nuclear power fields. To prevent such a severe accident due to the high-temperature and high-pressure environment, evaluating the residual stress of shot-peened materials is essential in evaluating the soundness of the material. Representative methods for evaluating residual stress include perforation strain gauge analysis, X-ray diffraction (XRD), and ultrasonic testing. Among them, ultrasonic testing is a representative, non-destructive evaluation method, and residual stress can be estimated using a Rayleigh wave. Therefore, in this study, the maximum compressive residual stress value of the peened Inconel 718 specimen was predicted using a prediction convolutional neural network (CNN) based on the relationship between Rayleigh wave dispersion and stress distribution on the specimen. By analyzing the residual stress distribution in the depth direction generated in the model from various studies in the literature, 173 residual stress distributions were generated using the Gaussian function and factorial design approach. The distribution generated using the relationship was converted into 173 Rayleigh wave dispersion data to be used as a database for the CNN model. The CNN model was learned through this database, and performance was verified using validation data. The adopted Rayleigh wave dispersion and convolutional neural network procedures demonstrate the ability to predict the maximum compressive residual stress in the peened specimen.https://www.mdpi.com/1996-1944/16/23/7406shot peenedresidual stressRayleigh waveconvolutional neural networkInconel 718 |
| spellingShingle | Yeong-Won Choi Taek-Gyu Lee Yun-Taek Yeom Sung-Duk Kwon Hun-Hee Kim Kee-Young Lee Hak-Joon Kim Sung-Jin Song Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network Materials shot peened residual stress Rayleigh wave convolutional neural network Inconel 718 |
| title | Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network |
| title_full | Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network |
| title_fullStr | Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network |
| title_full_unstemmed | Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network |
| title_short | Development of Maximum Residual Stress Prediction Technique for Shot-Peened Specimen Using Rayleigh Wave Dispersion Data Based on Convolutional Neural Network |
| title_sort | development of maximum residual stress prediction technique for shot peened specimen using rayleigh wave dispersion data based on convolutional neural network |
| topic | shot peened residual stress Rayleigh wave convolutional neural network Inconel 718 |
| url | https://www.mdpi.com/1996-1944/16/23/7406 |
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