The textile industry has a great role in the improvement of any country’s economy. Moreover, the ready-made garments need different coloured high yarn quality, so yarn should be rewinded on plastic cones for dyeing. However, manufacturers are facing the problem of tension variation during soft winding process that mainly affects the yarn quality. Consequently, to overcome the tension variation drawbacks, the attainment of constant optimal tension values is required in order to: (1) Increase the winding speed while maintaining the yarn quality, (2) Improve the dyeing quality, and (3) Reduce the water consumption during the dyeing process. In this paper, a proposed yarn tension control technique is introduced to upgrade the soft winding machine, thus maintain the yarn quality and improve the manufacturing capacity. The proposed technique has been tested on Polyester yarn samples classified as; fine, medium and coarse yarn counts, to cover most yarn sizes used in the industry. Arduino Mega 2560 controller is utilized to implement the proposed tension control. The results are compared to the conventional system to advocate the effectiveness and capability of the proposed technique in overcoming the trade-off between tension control and machine speed that occurs in conventional system using variable tension levels.
Several high-performance, industrial micro-electromechanical (MEM) devices, such as gyroscopes, magnetometers, high-Q resonators and piezoelectric energy harvesters, require wafer bonding and packaging under near-vacuum conditions. One very challenging aspect of the design, verification and characterisation of these devices is to predict their performance characteristics in the presence of any residual gases post-packaging. Such gases contribute to the energy losses resulting from device surfaces squeezing or sliding against the gas films within the device cavities. In this paper, we fully expose the modelling assumptions used in commercial FEM tools to estimate the squeezed-film damping (SFD) experienced by MEM devices that are packaged under near-vacuum conditions. We also explain the various meshing options to enable the extraction of the most accurate Q factors under existing SFD assumptions. In addition, we compare the computational results across a variety of commercial FEM codes against measurements obtained under realistic vacuum conditions for an industrial high-Q magnetometer. These measurements suggest that existing computational models may deviate by as much as 25% on Q factor values for gas flow regimes under operating cavity pressures of less than 1 Torr.
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