http://trama.deib.polimi.it/allegati/Kadaba_1990.pdf

Measurement of lower extremity kinematics during level walking

Recent advances in friction-stir welding : Process, weldment structure and properties

https://biodesign.seas.harvard.edu/files/biodesignlab/files/2015_-_polygerinos_-_soft_robotic_glove_for_combined_assistance_and_at-home_rehabilitation.pdf

Soft robotic glove for combined assistance and at-home rehabilitation

The comprehensive body of knowledge that has built up with respect to the friction stir welding (FSW) of aluminium alloys since the technique was invented in 1991 is reviewed The basic principles of FSW are described, including thermal history and metal flow, before discussing how process parameters affect the weld microstructure and the likelihood of entraining defects After introducing the characteristic macroscopic features, the microstructural development and related distribution of hardness are reviewed in some detail for the two classes of wrought aluminium alloy (non-heat-treatable and heat-treatable) Finally, the range of mechanical properties that can be achieved is discussed, including consideration of residual stress, fracture, fatigue and corrosion It is demonstrated that FSW of aluminium is becoming an increasingly mature technology with numerous commercial applications In spite of this, much remains to be learned about the process and opportunities for further research a

Friction stir welding of aluminium alloys

Three-dimensional kinematics of the human knee during walking.

Friction stir welding: Process, automation, and control

This correspondence presents a prototype of a powered hand exoskeleton that is designed to fit over the gloved hand of an astronaut and offset the stiffness of the pressurized space suit. This will keep the productive time spent in extravehicular activity from being constrained by hand fatigue. The exoskeleton has a three-finger design, the third and fourth fingers being combined to lighten and simplify the assembly. The motions of the hand are monitored by an array of pressure sensors mounted between the exoskeleton and the hand. Controller commands are determined by a state-of-the-art programmable microcontroller using pressure sensor input. These commands are applied to a PWM driven DC motor array which provides the motive power to move the exoskeleton fingers. The resultant motion of the exoskeleton allows the astronaut to perform both precision grasping tasks with the thumb and forefinger, as well as a power grasp with the entire hand.

An anthropomorphic hand exoskeleton to prevent astronaut hand fatigue during extravehicular activities

The use of inflatable structures has often been proposed for aerospace applications. The advantages of such structures include low launch weight and easy assembly. To consider using inflated fabric beams in aerospace applications, an accurate and efficient method of structural analysis must be developed. This paper develops a method of analysis for inflated fabric beams that is analogous to the shear-moment method. The bending model is developed from basic assumptions about the state of stress in the fabric of the inflated beam. The resulting model shows that the bending behavior of inflatable fabric beams is identical to conventional solid elastic beams as long as the beam fabric remains unwrinkled. Experimental data were obtained and verify the static bending model for beams with length/diameter ratios greater than 10.

Load‐Deflection Behavior of Space‐Based Inflatable Fabric Beams

Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding (VPPAW) process. Three areas of welding application were investigated: weld process modeling, weld process control, and weld bead profile analysis for quality control. Experiments and analysis confirm that artificial neural networks are powerful tools for analysis, modeling, and control applications. They are particularly attractive in view of their capabilities to process nonlinear and noisy data, learn from actual welding data, and execute at relatively high speed. It is shown that neural networks are capable of modeling parameters of the VPPAW process to on the order of 10% accuracy or better. The same was observed when neural networks were used to select welding equipment parameters and the resulting bead geometries were estimated. These performance figures suggest that a VPPA welding control system can be implemented based on neural network models and control mechanisms. >

Weld modeling and control using artificial neural networks

Experimental data for AA 6061-T6 friction stir welded at rotational and travel speeds ranging from 1000 to 5000 rev min−1 and from 290 to 1600 mm min−1 (11–63 ipm) are presented. The present paper examines the forces and torques during friction stir welding (FSW) with respect to mechanistic defect development owing to process parameter variation. Two types of defects are observed: wormholes and weld deformation in the form of significant excess flash material. A 3D numerical model, implemented using the computational fluids dynamics package Fluent, is used to simulate and investigate the parametric relationship of the forces and torques during FSW. In order to establish a mechanistic quantification of the FSW process, two mechanical models, the Couette and the viscoplastic fluid flow models, were simulated and compared with experimental data for AA 6061-T6.

Alvin M. Strauss

Papers

Friction stir welding: Process, automation, and control

An anthropomorphic hand exoskeleton to prevent astronaut hand fatigue during extravehicular activities

Load‐Deflection Behavior of Space‐Based Inflatable Fabric Beams

Weld modeling and control using artificial neural networks

Experimental defect analysis and force prediction simulation of high weld pitch friction stir welding