Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding

Abstract

In order to manufacture high quality microproducts, the precision control of injected plastic melt in the injection chamber during a microinjection process requires real-time tracking of the melt pressure when the melt passes through the nozzle. A novel type of adaptive Kalman filter algorithm based on F-distribution is proposed in this paper. This adaptive Kalman filter can switch the system between the steady state and transient state by comparing the differences of input data in F-distribution. By resetting the Kalman gain and other relevant parameters, the adaptive function guarantees the convergence of the filtered signal during the tracking process and tracks the moments which sudden changes occur in the pressure signature. The simulation experiment results show that the method can reduce the effect of measurement noise more quickly and effectively. The method is proven to be effective for microinjection molding applications.

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

“…Liu et al [58] used an EKF to improve the part quality in a micro-injection moulding process by controlling the pressure signature. The pressure signature is generated by a pressure transducer as the plastic melt passes through the nozzle.…”