GLASS GOB WEIGHT CONTROL USING AN IMAGE ANALYSING SYSTEM

摘要

This paper presents a computer vision system for measuring the weight of gobs during a glass forming process, and a control strategy to correct automatically any weight deviation from a given set-point. During the formation of molten glass gobs, several noise sources can cause a deviation in the weight from a predefined reference value. Among them, there is a random white-noise disturbance caused by the lack of synchronisation of mechanical devices, the periodic disturbances due to changes in the spinning direction of the tube inside the feeder, and some long-term drifts caused by variations in temperature and viscosity of the raw glass material. The gob weight measurement system developed is based on a monochrome CCD high-resolution camera and photo-detector for synchronising the frame acquisition. The molten glass provides the illumination, so a high contrast image is obtained with a bright object and dark background. Several image-processing algorithms are presented for reliable area estimation. Assuming that the gob is a symmetric geometry of revolution and uniform mass density, the proposed system estimates the weight of gobs with accuracy better than ± 0.75%. A learning weight control strategy is proposed based on a PI-repetitive control scheme. The weight deviation from a set point is used as a control signal to adjust the glass flow into the feeder. This regulation scheme allows effective weight control, cancelling mid and long-term effects. The tracking error, ± 1.5%, means a reduction of 40% when compared with a traditional PI controller.