首页|Findings from Chongqing University in the Area of Artificial Intelligence Report ed (A Hybrid Approach of Process Reasoning and Artificial Intelligence-based Int elligent Decision System Framework for Fatigue Life of Belt Grinding)
Findings from Chongqing University in the Area of Artificial Intelligence Report ed (A Hybrid Approach of Process Reasoning and Artificial Intelligence-based Int elligent Decision System Framework for Fatigue Life of Belt Grinding)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Chongqi ng, People's Republic of China, by NewsRx journalists, research stated, "Belt gr inding is widely used as the final step in the fabrication of fatigue-resistant surfaces of nickel-based superalloy components, and fatigue life after grinding is one of the most concerning issues. However, the response mechanism of fatigue life under different grinding parameter excitation conditions is not well under stood for a long time." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Chongqing, Innovation Group Sc ience Fund of Chongqing Natural Science Foundation, Basic Research Funds for Cen tral Universities. The news reporters obtained a quote from the research from Chongqing University, "In this study, a system framework of fatigue life prediction for nickel-based superalloy abrasive belt based on process reasoning and artificial intelligence algorithm is proposed. Based on the process reasoning method, the mathematical r elationship between grinding parameters and fatigue life is established. The equ ation is solved by RNN and LSMT algorithms embedded in the system, and the excit ation response model of process parameters to fatigue life is obtained. The resu lts show that the prediction accuracy of the system is high. The mean squared er ror (MSE) of the LSTM algorithm is below 0.0441, and the R-squared can be above 0.9956. In addition, experimental verification has been carried out, the observa tion of the specimen section shows that the process parameters have an effect on the initiation position, distribution, and crack length of the fatigue crack so urce, which are related to the stress concentration and residual stress distribu tion at the depth of the grinding scratches."
ChongqingPeople's Republic of ChinaA siaArtificial IntelligenceEmerging TechnologiesMachine LearningChongqing University
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