Techniques for suppression of vibration in cutting tools can save old machines and enhance
design flexibility in new manufacturing systems. Structural stiffness interaction, with the
use of smart materials, in an intelligent toolpost is investigated using the finite
element method. The results proved the limited use of lumped modeling in driving
conclusions and developing a toolpost system for dynamic response control. A transient
solution is obtained for the toolpost response in which a smart material actuator is
excited using pulse width modulation (PWM) for voltage generation to counteract
the radial disturbing cutting force. Calculations showed that error elimination
and transient response control require a minimum number of PWM cycles in
each force period. Time delay between the actuation force and voltage has an
adverse effect on error elimination, if it exceeds a certain limit. Increasing damping
within a reasonable range might not eliminate the transient response originated
by the voltage switching of the smart material actuator. The estimated static
voltage in error elimination cannot necessarily be used in dynamic switching. The
tool bit to actuator stiffness and tool carrier (holder) to actuator stiffness ratios
are both preferred to be above ten when space and weight limitations do not
exist.
Stabilization of a single wheel mobile robot attracted researcher attentions in robotic area. However, the budget requirements for building experimental setups capable in investigating isolated parameters and implementing others encouraged the development of new simulation methods and techniques that beat such limitations. In this work we have developed a simulation platform for testing different control tactics to stabilize a single wheel mobile robot. The graphic representation of the robot, the dynamic solution, and, the control scheme are all integrated on common computer platform using Visual Basic. Simulation indicates that we can control such robot without knowing the detail of it's internal structure or dynamics behaviour just by looking at it and using manual operation tactics. Twenty five rules are extracted and implemented using Takagi-Sugeno's fuzzy controller with significant achievement in controlling robot motion during the dynamic simulation. The resulted data from the successful implementation of the fuzzy model are used to utilize and train a neurofuzzy controller using ANFIS scheme to produce further improvement in robot performance
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