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Modification of tribological performance of air motor friction dynamics

Marumo R.

Systems Science

2006

Vol. 32, no 1

71-87

This paper considers the investigations into adhesion, contact mechanics metal erosion effects, wear and tear as a result of the effects of frictional forces. Mechanical components rely on friction for the transformation and delivery of energy from point A to point B. This requires the knowledge of combined energies as well as their associated dynamic models and ancillary parameters. Adhesion, contact, friction and wear are major problems limiting both the fabrication yield and lifetime of any devices. Since it is the area of real contact that determines the sliding friction, adhesion interaction may strongly affect the friction force even when no adhesion can be detected in a pull-off experiment. Therefore, a good scientific dynamic modelling of friction forces is a prerequisite for the understanding and monitoring of friction adverse effect on mechanical systems for good maintenance purposes.

Air motor system modelling using extended neural network algorithms

Marumo R., Tokhi M. O.

Systems Science

2005

Vol. 31, no 3

87-101

This paper investigates the development of neuro-modelling approaches for a highly non-linear system. The work is motivated by the fact that the response of a pneumatic drive is very slow, which leads to inability of the system to attain set points due to high hysteresis. Also the dynamic model of the pneumatic system is highly non-linear, which greatly complicates controller design and development. To address these problem areas, two streams of research efforts have evolved. These are: using conventional methods to develop a modelling and control strategy and adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three main regions, namely low speed, medium speed and high speed. The system is highly non-linear in the low speed region and hence a neuro-modelling approach is proposed.