Balancing of Slider-Crank Mechanisms

  • Vigen ArakelianEmail author
  • Sébastien Briot
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 27)


In this Chapter, the balancing methods of slider-crank mechanisms are presented. In Sect. 5.1, the generalized Lanchester balancer is proposed. It allows the balancing of primary and secondary shaking forces of off-set slider-crank mecha-nisms. Section 5.2 deals with the solution of the problem of the shaking force and shaking moment balancing of slider-crank mechanisms based on the properties of the Watt gear-slider mechanism. In the examined system, the output gear mounted on the frame meshes with a second identical gear attached to the connecting rod of the slider-crank mechanism. The output gear thus rotates at approximately twice the speed of the input crank and has the same angular acceleration as the connecting rod. These properties are used for the generation of the movements of counterweights intended for balancing. Such a solution allows significant minimization of the shaking force and shaking moment of the mechanism. It also provides the conditions for balancing with only a small increase of the input torque. The efficiency of the suggested method is illustrated by a numerical example.

The Sect. 5.3 presents a solution for improving the balancing of double slider-crank mechanical systems. In these systems the shaking force balancing is achieved by two identical slider-crank mechanisms, which execute similar but opposite movements. Section 5.4 disclose with the simultaneous inertia force/moment balancing and torque compensation of slider-crank mechanisms by using a cam/spring auxiliary mechanism.

At the end of the Chapter (Sect. 5.5), the shaking force and shaking moment balancing of slider-crank mechanisms via optimal generation of the input crank rotation is disclosed.


Inertia Force Angular Acceleration Linear Acceleration Input Torque Motion Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institut National des Sciences Appliquées (INSA), Rennes, France, and Institut de Recherche en Communications et Cybernétique de Nantes (IRCCyN), Nantes, FranceRennesFrance
  2. 2.Institut de Recherche en Com. et Cybernétique de NantesCNRSNantesFrance

Personalised recommendations