|dc.description.abstract||This dissertation is focused on the modeling of one kind of braiding machine and its key part, the carrier. The carrier is a small mass-spring-damping tensioning system, which defines the characteristics and response of the braiding point on an operating braiding machine. The yarn tension from a carrier versus displacement characteristic is first derived, and experimentally verified. Based on this result, the braiding point motion envelop is investigated in order to determine and explain the expected range of small braid point motion and oscillation that occur about the steady state. A material flow system model is derived for the braiding process at the braiding point. Three mathematical models are created and combined to form an integrated model of the entire braiding process. Using machine vision routines developed in this dissertation, a control program was used to monitor and record the braiding point motion and compare it with analytical results. A new noninvasive machine vision sensor was developed, for use with a piecewise PI controller on a separate take up motor using the position data acquired from a machine vision sensing loop. Correlated experiment and simulation response validate the mathematical model, which is similar to a first order liquid level system.
Braiding is a manufacturing process for making tubular products. A yarn or tow tensioning system, a carrier, is required that consists of two small pulleys, two springs and a ratchet with the ratchet gear on the spool with wound yarn. The tension coming from a single carrier is nearly constant, varying within an acceptable range during braided product formation and releasing a discrete amount of material from a spool when an upper limit on the tension is reached. The releasing frequency depends on the towed speed of the yarn. A mathematical model of tension versus yarn displacement of a standard package tensioning system is presented. The response before ratchet release is a series of piecewise linear kinematic regions that include two separate spring preload regions, a single spring tensioning region, and a two spring tensioning regions. During the ratchet releasing, the system is modeled as two regions of a single degree-of-freedom dynamic model, releasing region and impact region. Ratchet reengagement that incorporated impact with an elastic yarn was required to improve model accuracy of response.
The 32-carrier braiding machine used in this dissertation included a braiding motor, a take up motor and 32 carriers with corresponding yarns. The tension coming from single yarn is nearly constant, especially, when compared with the tension of the rope towed by the take up motor during the braiding process. The length of material releasing from the carriers affects the motion of braiding point. The tension of a single yarn changes because of yarn releasing. The releasing materials and releasing tension of the yarn cause the oscillation of the braiding point. A mathematical model of the braiding process close to the braiding point region is presented as a quasistatic process. The response after ratchet release is shown to be the reason for oscillation of the braiding point in the steady state. The amount released determines the maximum range of the locus of the braiding point. And the releasing frequency determines the frequency of oscillation. The locus of the braiding point moves on an “ellipsoidal cap”. Since the releasing of yarn is almost instantaneous, the motion of braiding point rapidly jumps from one point to another.
Controlling braiding angle is important for controlling the quality of braiding products. Controlling the position of the braiding point also controls the braiding angle. In practice, it takes a long time for a braiding point to find its steady state position after startup (if the two motor keep a constant speed). This can lead to a large amount of wasted material and lost production. The braiding point pattern includes 32 yarns, rope and the convergent zone. This machine vision pattern, viewed with a USB camera, changes from moment to moment during braiding process. Setting up the corresponding threshold for pattern change is important, and is based on the color of yarn and lighting condition (illumination) of the background. The machine vision algorithm senses the braiding point using the geometric pattern matching method in Labview. The PI controller is designed to drive the take up motor in order to reduce the settling time of the braiding point, using a feedback position signal from the machine vision system. Experimental results confirms this technique to substantially reduce the amount of material waste.||en