This paper presents a finite element analysis of a composite shaft under dynamic variable fatigue loading. The object of this study is the behavior of the fatigue life of a composite shaft under dynamic variable fatigue loading. The fatigue life of the shaft is then determined by analyzing the stress distribution and its effect on the material's fatigue strength. The investigation of fatigue behavior involves evaluating factors such as stress concentrations, fatigue crack initiation and propagation, and the cumulative damage caused by cyclic loading. The study explores the impact of biaxial loading on the shaft's fatigue performance and provides insights into its significance in predicting fatigue life and it is 10e7 cycles. Furthermore, a damage indicator is predicted to assess the accumulated damage and monitor the progression of fatigue-related degradation. This indicator serves as a valuable tool for predicting the remaining useful life of the composite shaft. The equivalent alternative stress is calculated to characterize the combined effect of different loading conditions on the fatigue life of the composite shaft. By quantifying the stress level and variations experienced by the structure, this parameter allows for a comprehensive assessment of the fatigue performance under variable loading scenarios 250 N. The findings of this research contribute to the understanding of fatigue behavior in composite shafts under dynamic variable fatigue loading. The insights gained from the fatigue life investigation, biaxiality indication, damage prediction, and equivalent alternative stress calculation can aid in optimizing design considerations, maintenance planning, and enhancing the reliability and durability of composite shafts in various engineering applications
With the constant evolution of e-manufacturing technologies, there is a clear trend for e-maintenance that involves the integrating of ICT (Information and communication technologies) within the maintenance strategy. This leads to highly sophisticated and complex machinery, which increases the demand for expertise. Unfortunately, a company could always lose the expertise due to experts’ retirement, change of occupation or death. This motivates us in this work to develop an e-maintenance model that enables organizations to exploit expert’s knowledge in the process of machine fault diagnosis. This paper focuses on the building of a Knowledge-Based System (KBS) in order to capture the experts’ knowledge to be permanently kept and cannot be disparaged due to lack of practice. An optimal AI-based tool is proposed that aims at accurate values to retrieve information from KBS, which describes the alarms to diagnose the failure of the machine. An accurate analysis is carried out that yields insight into the impact of KBS on the ability of fault diagnosis. The results illustrate the high-performance of the proposed approach in handling the KBS's data associated.
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