Robotic-Assisted Measurement of Fabrics for the Characterization of the Shear Tension Coupling

Abstract

Industrial use of composite materials requires an increasingly advanced knowledge of technical textiles mechanical properties to control the manufacturing process and guarantee the performances of the finished products. Among the qualities that influence greatly the shaping process, theshear deformability is key for the forming of complex composite parts with double curves geometries. On the other hand, the stiffening of the behavior as the shearing rise is responsible for the occurrence of the wrinkling defect. This shearing behavior of the textile reinforcement is difficult to determinebecause it is non-linear and it coexists with a tensile stiffness of the fibers that is several orders of magnitude higher. Furthermore, shear and tension are coupled due to the weaving of the textiles. Now, few experimental methods have been proposed to measure the tension behavior of fabric as a function of its shear level because dedicated devices are needed for this investigation, capturing the shear-tension coupled behavior of fabric is then a difficult task. This paper deals with the robotization of the fabric shear-tension effect characterization. A KUKA robot associated with a force/torque sensor is utilized, taking advantage of its benefits in the ability to control the state of yarn tensions during shear tests while keeping track of the desired trajectory as enabled by the hybrid position-force control feature. This ensures precise positioning of a sample fabric and accurate contact forces. An anisotropic hyperelastic constitutive model for fabrics, based on the continuum theory of mechanics that takes into account the shear-tension coupling effect was formulated analytically and numerically simulated using Matlab software. An experimental test was then implemented to validate the proposed model. The results from a uni-axial tensile test and shear test under constant uni-axial tensile loading were obtained and analyzed to characterize the test sample. The model parameter identification was performed and presented in detail.