Characterizing residual stress on pipework weld repairs using machine learning

使用机器学习表征管道焊接修复的残余应力

• 批准号: 2891480
• 金额: --
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891480
• 关键词: Characterizing; residual; stress; pipework; weld

Nuclear power plant systems are constructed by joining pressure vessels and pipingcomponents using modern welding processes. Welded structures may also be subjected to weld repair eitherduring fabrication to mitigate manufacturing defects, or during service to maintain the original design life, orto provide life extension. The PhD project will review the latest developments in data mining and machine learningmethods, identify baseline parameters controlling residual stresses at weld repairs, collect high qualitytraining data using data mining methods, develop/optimise a suitable machine learning tool, train the toolusing the mined data, validate the outputs against independent measurements, and create residual stresscharacterisation tool with a user-friendly interface for engineers in industry.A major limitation in using ANN for prediction of residual stress in weldments is the shortage of high qualitymeasurement data for training. The innovation and challenge of the proposed PhD project will be to exploitthe opportunity of combining measurement results and synthetic data from validated simulations in orderto predict/estimate residual stress profiles in families of repair weldments (that lie within the training dataparameter envelope).

核电厂系统是通过使用现代焊接工艺连接压力容器和管道组件来构建的。焊接结构也可以进行焊接维修，以减轻制造缺陷，或者在维持原始设计寿命的服务期间，ORO提供寿命延长。博士项目将审查数据挖掘和机器学习方法的最新进展，确定控制焊接维修时控制残留应力的基线参数，使用数据挖掘方法收集高质量的培训数据，开发/优化合适的机器学习工具，训练通过挖掘数据，训练工具，验证输出针对独立测量的验证，并使用用于行业工程师的用户友好界面来创建残留的应力示声工具。使用ANN预测焊接中残留应力的主要限制是用于训练的高质量测量数据的短缺。拟议的博士学位项目的创新和挑战将是利用从经过验证的模拟中结合测量结果和合成数据的机会，以便预测/估计维修焊接家族中的残余应力概况（训练数据仪内层中）。