Abstract
The article is focused on the revelation of using AI technique to detect cracks in shaft using ANFIS, ANN based on error percentage comparing with experimental work. The effectiveness of crosswise loaded fixed-fixed shaft with multiple cracks is contemplated using theoretical and experimental analysis in the article. The presence of fractures with its positions and dimensions on vibration domain is identified considering the consequence of curtailment in stiffness. The fundamental frequencies including their mode patterns in varying positions and intensities are estimated. These outcomes received from the analytical model have applied by ANFIS and ANN utilizing the modal frame. The parameters so as first three fundamental frequencies including their mode shapes for several positions and intensities of the shaft are rendered to ANFIS and ANN network separately. The test model including the sustainable error checks the authenticity of the AI techniques (ANFIS and ANN design). It is concluded that the rate of average error in ANFIS and ANN based on the experimental investigation is 2.17% and 3.5%, respectively. The existing approach is simple for preparing a condition-monitoring model of the shaft applying in a structure.
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Nanda, J., Das, L.D., Choudhury, S., Parhi, D.R. (2020). Revelence of Multiple Breathing Cracks on Fixed Shaft Using ANFIS and ANN. In: Deepak, B., Parhi, D., Jena, P. (eds) Innovative Product Design and Intelligent Manufacturing Systems. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2696-1_58
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DOI: https://doi.org/10.1007/978-981-15-2696-1_58
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