Twin Shaft-Geared Crankweb Crankshaft System with Optimization of Crankshaft Dimensions Using Integrated Artificial Neural Network-Multi Objective Genetic Algorithm

  • Anierudh Vishwanathan Birla Institute of Technology and Science, Pilani-333031(India)

Abstract

This paper suggests a novel design of a multi cylinder internal combustion engine crankshaft which will convert the unnecessary/extra torque provided by the engine into speed of the vehicle. Transmission gear design has been incorporated with crankshaft design to enable the vehicle attain same speed and torque at lower R.P.M resulting in improved fuel economy provided the operating power remains same. This paper also depicts the reduction in the fuel consumption of the engine due to the proposed design of the crankshaft system. In order to accommodate the wear and tear of the crankshaft due to the gearing action, design parameters like crankpin diameter, journal bearing diameter, crankpin fillet radii and journal bearing fillet radii have been optimized for output parameters like stress which has been calculated using finite element analysis with ANSYS Mechanical APDL and minimum volume using integrated Artificial Neural Network-Multi objective genetic algorithm. The data set for the optimization process has been generated using Latin Hypercube Sampling technique.

Keywords

modified crankshaft, twin shaft, engine revving, Artificial Neural Network, optimisation
Published in Onlinefirst
19-01-2017
How to Cite
VISHWANATHAN, Anierudh. Twin Shaft-Geared Crankweb Crankshaft System with Optimization of Crankshaft Dimensions Using Integrated Artificial Neural Network-Multi Objective Genetic Algorithm. Periodica Polytechnica Transportation Engineering, [S.l.], 2017. ISSN 1587-3811. Available at: <https://pp.bme.hu/tr/article/view/10188>. Date accessed: 21 nov. 2017. doi: https://doi.org/10.3311/PPtr.10188.
Section
Articles