Glass fiber reinforced polymers (GFRP) is a new material with many advanced features in terms of strength, light weight, anti-corrosion ability on salty environment, which may replace steel. In this work, we present the technology of manufacturing of GFRP in bar form: pultrusion technology. The production line at the factory is imported from abroad. The objective of the research is to step by step mastering the technology and fully master the production line system of GFRP in bar form in Vietnam. We have fabricated successfully a product of high applicability, which has great potential for development (GFRP in bar form with large diameter, 20 mm). Pultrusion is one of technologies to fabricate the polymer composites used in many industries such as in aerospace, automotive and construction ones industries. The high performance pultruded products that are produced by this technique offer high fiber content of at least 70%. In order to produce high quality pultruded profiles, there are variables such as fiber impregnation, resin viscosity, pulling speed and curing temperature that have to be considered and these requests are discussed in this study. The aim of the present work is evaluating elastic properties like Young's modulus and Poisson's ratio from analytical methods such as Rule of mixture, Halpin-Tsai, Nielsen, Chamis and Hashin elastic models and compared with experiment results. The result shows a big difference. The mechanical characteristics of the GFRP D20 bar depend not only on the composition of components (fiber and epoxy) but also on the manufacturing technology. We propose research further direction: optimize the technological element in manufacturing GFRP bar with large diameter.

—Recently, many technological improvement apply in the discovery of various designs of haptic devices. Several mechanism structures including serial, parallel, and hybrid-kinematic manipulators can be considered for making a haptics device. The most successful haptic mechanisms are parallel-type, because of low moving inertia, large force reflection, and high stiffness. This research shows the 6-DOF parallel haptics device based on the parallel mechanism using a translation driver motor mounted on each leg. Firstly, we introduce a 6-DOF parallel mechanism using a translation driver motor haptics device model. Due to the unsure parameters, we focus on solving the mathematics model with the nonlinear parameters of the 6-DOF parallel mechanism. Secondly, to fix the kinematics and dynamics nonlinear uncertainties parameters, the SMCNN controller for 6-DOF parallel mechanism application using a translation driver motor is designed. The Sliding model control base on artificial intelligence neural network is used to calculate the unsure factors. In this technique, to prove the stability of the system the Lyapunop theory is used. Finally, the authors the simulation results of two control algorithms with different uncertain components are presented and comparing them to demonstrate the effectiveness of the new control method. The control method is demonstrated by way of implementing the set of rules in artificial surroundings with realistic parameters, in which the received consequences are fairly promising. The obtained from SMCNN algorithm results are highly promising and accurate.

This research the authors design the intelligent control for 3-DOFs lower limb rehabilitation robot base on the complex dynamics equation. The Force Feed-Forward Method (FFM) is promote to control the of 3-DOFs lower limb rehabilitation robot including dynamics characteristics. The robot can sense the force of the therapist which exerted on the robot and patient's leg, then produces necessary forces through joints at the hip, knee, and ankle. The force feedforward controller is used to compensate the force generated by the therapist to perform patient-active exercises. In this paper, firstly authors briefly introduce 3-DOFs lower limb rehabilitation robot, next the kinematics and dynamics equation of 3-DOFs lower limb rehabilitation robot established base on Lagrange-Euler method are presented, and then the control method is introduced. Last, the performance of the proposed control methods has been confirmed by numerical simulations of the robot in all three joints: hip, knee, and ankle.

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