Integration of the vertical warp stop motion positioning in the model-based self-optimization of the weaving process

Abstract

The warp tension is a critical variable of the weaving process. If the warp tension is too high or too low, the weaving process will be interrupted. In order to find a suitable setting for the weaving machine, the experience of the operator is needed. Self-optimization routines can support the operator in finding optimal settings. Within this paper, the model-based self-optimization of the weaving process developed at Institut fur Textiltechnik der RWTH Aachen University is presented. The self-optimization routine uses an automatic design of experiment to generate data for a full quadratic regression model of the characteristic values of the warp tension. Three weighted quality criteria are used to optimize the machine settings within given boundaries. An improvement is proposed by integrating the vertical warp stop motion position as a factor with high impact on the warp tension. The vertical warp stop motion position is automated and integrated into the optimization process. The adjusted routine is validated on an air jet weaving machine. The test results show that the integration of the warp stop motion position into a self-optimization routine leads to a 35% reduction of tension in the warp yarns. Compared to the existing routine, the integration of the warp stop motion position leads to a 23% higher effect on the warp tension as the target value of the optimization. The statistical validation shows that the quality of the used regression model is high. The described system also reduces the setup time of a weaving machine. Economically, the improvements mean a reduction of production costs by 22%, when producing small lot sizes. The system therefore contributes to the competitiveness of weaving mills in high-wage countries.