This paper presents a novel design of a fingertip mechanism for detecting the slippage of the grasped object under two different types of dynamic load. This design is to be used with an underactuated triple finger artificial hand based on pulleys-tendon mechanism and the grasped object is designed in a prism shape with three DC motors with unbalance rotating mass to generate the excitation in the object, these motors are distributed symmetrically on the faces of the object. This prism shaped object is connected to a rope type pulling system to force the object to slip under quasi-static load condition. The mathematical modeling has been derived for the proposed design to generate the signal of contact force components ratio through using the conventional sensors signals with the aid of matlab–simulink software. The experimental results are discussed in comparison with the physical aspect of slippage phenomenon and they show good agreement with the physical definition of the slippage phenomenon.

This study treats with the transverse vibration of polypropylene random-copolymer (PP-R) pipes caused by fluid flow inside them assuming pinned connections at both ends. The effect of inclination angle, aspect ratio (the ratio of length to outside diameter) and temperature variation on dynamic response of inclined pipe containing flowing fluid with different velocity was investigated. The Euler-Bernoulli formula for beam theory was adopted to model the inclined pipe. An analytical model has been prepared to calculate the dynamic response of the pipe, taking into account pipe weight, thermal effect, inclination angle, aspect ratio, and fluid flow velocity, using the integral transform techniques (ITT) by utilizing a combining of Laplace and Fourier transforms and their inverses. The results demonstrate that the prepared analytic model is a powerful tool to obtain the dynamic characteristic of pipe conveying fluid. Moreover, the results showed that the dynamic deflection was strongly affected by the change in the values of inclination angle, pipe temperature, aspect ratio, and fluid velocity, where there was a significant increase in pipe deflection with increasing temperature and aspect ratio. and fluid velocity, while a decrease in the deflection value is observed with an increase in the angle of inclination in the range of 0-90°. The findings proved that the thermal effect becomes more important than the fluid velocity at high aspect ratios. The same case applies to the angle of inclination, as its effect on the pipe deflection increases at high aspect ratios. These results were limited to the fundamental (first) mode and can be useful for engineering component design. The main contributions of this work are to find the combined effect of inclination angle, thermal loading, and aspect ratio on the deflection of the pipe, in addition to preparing an analytical model to calculate this deflection.

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This paper presents an artificial neural network (ANN) trained on the patterns of slip signals; these patterns were generated by using conventional sensors with a novel design of fingertip mechanism for detecting the slippage of a grasped object under different types of dynamic loads. This design is to be used with an underactuated triple finger artificial hand based on the pulleys-tendon mechanism. The grasped object is designed in a prism shape with three direct current motors with unbalance rotating mass to generate excitation in the object. Also, this object is covered with different types of surface materials, namely, spongy rubber, glass, and wood. Three types of external loads are used to disturb the grasping process represented by quasi-static pulling on the object, the dynamic load on the object, and on the artificial arm in separate form. The mathematical modeling has been derived for the proposed design to generate the signal of contact force components ratio through using the conventional sensor signals with the aid of Matlab-Simulink software. The ANN has been trained on the basis of the patterns of force component ratio signals at slippage occurrence, in order to detect slippage and then prevent it without the need for any knowledge about the surface properties of the grasped object. The experimental results are discussed in comparison with the physical aspect of the slippage phenomenon, and they show good agreement with the physical definition of the slippage phenomenon. In addition, the network evaluation results are discussed with different parameters that govern the controller operation, such as network error, classification efficiency, and delay in response time.

This paper presents a theoretical and experimental study to control grasping force of specific artificial hand (Otto Bock 8E37), which it uses by amputees. The hand has two rigid fingers actuated by a DC motor through a multigears system. The aim of this work is to give the amputees a feeling of slipping while the hand grasping an object. The mathematical model has been derived to simulate the hand mechanism and analyze the generated signal of contact force between fingertip and the grasped object through a slippage phenomenon. The experimental work consisted of modifying the artificial hand design to aid load cell mounting process in order to measure the grasping force indirectly, then acquiring the measured signal to the PC. An artificial neural network (ANN) was trained on the patterns of the force signals. These patterns were prepared by using force sensors with modified design of the artificial hand for detecting the slippage of the different shapes grasped object. The Neural Network training results have been evaluated and discussed under different conditions, which affect the controller operation such as network error, classification percentage and the response time delay.