A nonlinear biphasic fiber-reinforced porohyperviscoelastic (BFPHVE) model of articular cartilage incorporating fiber reorientation effects during applied load was used to predict the response of ovine articular cartilage at relatively high strains (20%). The constitutive material parameters were determined using a coupled finite element-optimization algorithm that utilized stress relaxation indentation tests at relatively high strains. The proposed model incorporates the strain-hardening, tension-compression, permeability, and finite deformation nonlinearities that inherently exist in cartilage, and accounts for effects associated with fiber dispersion and reorientation and intrinsic viscoelasticity at relatively high strains. A new optimization cost function was used to overcome problems associated with large peak-to-peak differences between the predicted finite element and experimental loads that were due to the large strain levels utilized in the experiments. The optimized material parameters were found to be insensitive to the initial guesses. Using experimental data from the literature, the model was also able to predict both the lateral displacement and reaction force in unconfined compression, and the reaction force in an indentation test with a single set of material parameters. Finally, it was demonstrated that neglecting the effects of fiber reorientation and dispersion resulted in poorer agreement with experiments than when they were considered. There was an indication that the proposed BFPHVE model, which includes the intrinsic viscoelasticity of the nonfibrillar matrix (proteoglycan), might be used to model the behavior of cartilage up to relatively high strains (20%). The maximum percentage error between the indentation force predicted by the FE model using the optimized material parameters and that measured experimentally was 3%.

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The purpose of this work is to design and fabricate a balanced passive robotic arm with the capability of applying variable mass to the end-effector in order to upper limb rehabilitation. To achieve this purpose, the first step is associated with establishing a robot structural design in the CAD environment. The next step is focused on developing the kinematic model based on the degrees of freedom and joint range of motion of the lower legs. Thereafter, the potential energy functions are determined for the springs and weight of components applied in the mechanism. The genetic algorithm is employed as a proper optimization program to extract the system design parameters, including the spring stiffness coefficients and their placement positions within the system. A prototype is fabricated for a balanced robot, and the end-effector mass variations are utilized to develop an adjustable balance capability. To create balance in the system, several items are designed, consisting of a control panel, two electric motors, and an electronic processor. This situation provides an equivalent force equal to the weight of selected mass from the end-effector to the user's hand. (It is done by a reverse process.) The actual mass required for robot balance is compared to the mass defined in the simulation environment. The evaluation results indicate that it is possible to create an optimized balance by using the simulation outputs.