{"title":"利用有效应力强度因子和人工神经网络预测单次拉伸过载后裂纹扩展行为","authors":"Anindito Purnowidodo, Redi Bintarto, M.A. Choiron","doi":"10.1016/j.ijpvp.2025.105504","DOIUrl":null,"url":null,"abstract":"<div><div>This study presents an artificial neural networks (ANN) Model for predicting fatigue crack growth behavior under variable amplitude loads, specifically for negative and zero stress ratios with a single tensile overload. Using the effective stress intensity factor (<em>ΔK</em><sub><em>eff</em></sub>) as the primary input, the ANN Model estimates crack growth and fatigue life based on known crack length and increments. Results show that the Model provides accurate predictions of <em>ΔK</em><sub><em>eff</em></sub> and crack growth behavior across various loading conditions. The ANN approach offers a practical tool for assessing fatigue life in engineering applications, even with limited datasets.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"216 ","pages":"Article 105504"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of crack growth behavior after a single tensile overload using the effective stress intensity factor and artificial neural networks\",\"authors\":\"Anindito Purnowidodo, Redi Bintarto, M.A. Choiron\",\"doi\":\"10.1016/j.ijpvp.2025.105504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study presents an artificial neural networks (ANN) Model for predicting fatigue crack growth behavior under variable amplitude loads, specifically for negative and zero stress ratios with a single tensile overload. Using the effective stress intensity factor (<em>ΔK</em><sub><em>eff</em></sub>) as the primary input, the ANN Model estimates crack growth and fatigue life based on known crack length and increments. Results show that the Model provides accurate predictions of <em>ΔK</em><sub><em>eff</em></sub> and crack growth behavior across various loading conditions. The ANN approach offers a practical tool for assessing fatigue life in engineering applications, even with limited datasets.</div></div>\",\"PeriodicalId\":54946,\"journal\":{\"name\":\"International Journal of Pressure Vessels and Piping\",\"volume\":\"216 \",\"pages\":\"Article 105504\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Pressure Vessels and Piping\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0308016125000742\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Pressure Vessels and Piping","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0308016125000742","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Prediction of crack growth behavior after a single tensile overload using the effective stress intensity factor and artificial neural networks
This study presents an artificial neural networks (ANN) Model for predicting fatigue crack growth behavior under variable amplitude loads, specifically for negative and zero stress ratios with a single tensile overload. Using the effective stress intensity factor (ΔKeff) as the primary input, the ANN Model estimates crack growth and fatigue life based on known crack length and increments. Results show that the Model provides accurate predictions of ΔKeff and crack growth behavior across various loading conditions. The ANN approach offers a practical tool for assessing fatigue life in engineering applications, even with limited datasets.
期刊介绍:
Pressure vessel engineering technology is of importance in many branches of industry. This journal publishes the latest research results and related information on all its associated aspects, with particular emphasis on the structural integrity assessment, maintenance and life extension of pressurised process engineering plants.
The anticipated coverage of the International Journal of Pressure Vessels and Piping ranges from simple mass-produced pressure vessels to large custom-built vessels and tanks. Pressure vessels technology is a developing field, and contributions on the following topics will therefore be welcome:
• Pressure vessel engineering
• Structural integrity assessment
• Design methods
• Codes and standards
• Fabrication and welding
• Materials properties requirements
• Inspection and quality management
• Maintenance and life extension
• Ageing and environmental effects
• Life management
Of particular importance are papers covering aspects of significant practical application which could lead to major improvements in economy, reliability and useful life. While most accepted papers represent the results of original applied research, critical reviews of topical interest by world-leading experts will also appear from time to time.
International Journal of Pressure Vessels and Piping is indispensable reading for engineering professionals involved in the energy, petrochemicals, process plant, transport, aerospace and related industries; for manufacturers of pressure vessels and ancillary equipment; and for academics pursuing research in these areas.