A Crank Mechanism with Elastic Joints Having Preset Characteristics

2021 ◽  
Vol 50 (5) ◽  
pp. 388-395
Author(s):  
A. N. Zotov ◽  
A. S. Sviridov
2021 ◽  
Vol 103 (3) ◽  
Author(s):  
Suresh Ramasamy ◽  
Ross L. Hatton
Keyword(s):  

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.


Author(s):  
Maria T. Orvananos-Guerrero ◽  
Mario Acevedo ◽  
Claudia N. Sanchez ◽  
Nicola Ivan Giannoccaro ◽  
Paolo Visconti ◽  
...  
Keyword(s):  

2012 ◽  
Vol 487 ◽  
pp. 608-612 ◽  
Author(s):  
Chih Cheng Kao

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.


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