Simultaneous optimization of thermal and electrical conductivity of high density polyethylene-carbon particle composites by artificial neural networks and multi-objective genetic algorithm

2022 ◽  
Vol 201 ◽  
pp. 110956
Author(s):  
Miguel García-Carrillo ◽  
Adriana B. Espinoza-Martínez ◽  
Luis F. Ramos-de Valle ◽  
Saúl Sánchez-Valdés
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Electrical Conductivity ◽  
Artificial Neural Networks ◽  
High Density Polyethylene ◽  
Carbon Particle ◽  
High Density ◽  
Simultaneous Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Optimal kinematic synthesis of 6 bar rack and pinion Ackerman steering linkage

2020 ◽  
pp. 095440702097449
Author(s):  
Debendra Pradhan ◽  
Krishanu Ganguly ◽  
Biswajit Swain ◽  
Haraprasad Roy
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Simultaneous Optimization ◽  
Kinematic Synthesis ◽  
Track Width ◽  
Rigid Link ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Steering Mechanism ◽  
Selection Of

In this article, synthesis and simultaneous optimization of a steering mechanism is proposed for enhancing the cornering performance of a Formula Students competition car. A planar six-bar steering mechanism is synthesized assuming it as two separate slider-crank arrangements with a rigid link between the sliders. Numerical optimization is performed using multi-objective genetic algorithm (MOGA), which includes minimization of differences between both slider displacements and summation of deviations from true Ackerman geometry for a set of steering inputs. The selection of various parameters for running MOGA is well established based on iterative way. The seven variables (such as wheelbase and track width lengths, tie rod, tie-arm etc.) are optimized and used to construct the Ackerman steering geometry. Finally, the outer to inner tyre rotations of obtained geometry is calculated and compared with the predefined targeted values of actual Ackerman criteria.

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