Efficiency enhancement of thresher by auto-controlling the cylinder clearance

Rahul S. Warghane () and Rajkumar Easwara Pillai ()
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Rahul S. Warghane: Vellore Institute of Technology- VIT University
Rajkumar Easwara Pillai: Vellore Institute of Technology- VIT University

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 4, 2066-2079

Abstract: Abstract The impact shelling of grains in the threshing drum leads to the generation of undamped vibration in the system. The induced vibration results in deformation of the rasp bar member at the concave section and produces disturbance in clearance gap. The mathematical modelling of the system is formulated on the basis of dynamic analysis at the concave section. The mathematical model representing the deformation due to dynamic impedance gives the deflection equation of the system. The ‘matlab’ simulation is performed on the mathematical model to trace the maximum deflection at the concave section. The simulator deflection is compared with the deformation obtained by finite element analysis. The finite element analysis is performed on the mechanical design model with structural static and dynamic loading. The mapping of the deflection coefficient gives the positive correction coefficient of 0. 9854. The deflection equation is programmed into the Arduino. The stress transducer records the deflection over the concave section and simulates the drive for auto-controlling the clearance gap. The real-time monitoring of deflection is noted and processed for controlling the thresher encoder motor. The interfacing of deflection equation imparts real-time controlling of threshing parameters &commits the highest efficiency. The proposed approach aims at providing solutions for the future of smart cities by minimizing crop residue and providing socio-economic opportunities.

Keywords: ‘matlab’ simulation validation; Nodal deflection method; Rasp-bar clearance auto-control; Smart city; Thresher efficiency enhancement; Thresher vibration modelling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-023-01912-5

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