On‐line multivariate optimization of injection molding

Abstract

An auxiliary process controller was designed, implemented, and validated for on-line process and quality optimization. The objective function included terms related to the process variation, model uncertainty, and control energy. The controller architecture relied on characterized models including both process transfer functions and principal components analysis to perform online optimization in parallel with the physical molding process. New process and quality observations were input to the controller to update the models and provided new settings for the machine controller. Experimentation included characterization with a D-optimal design of experiments followed by a validation to measure the controller's performance with respect to controller stability, extrinsic material variation, cycle time reduction, and other common manufacturing goals. In every case, the controller was able to reduce the value of the objective function while also improving the part dimensions relative to tight tolerance specifications. While characterization experiments could be costly, the use of the resulting process models greatly speeds convergence and facilitates the consideration of various cost and quality terms in the objective function. POLYM. ENG. SCI., 55:2743-2750, 2015

Intelligent Injection Molding on Sensing, Optimization, and Control

“…e model-free optimization (MFO) algorithm is also an effective method. Johnston et al [160] and Yang et al [161] developed an online, model-free process optimization method and obtained ideal convergent results. A systematic approach that combined digital image processing (DIP) and model-free optimization (MFO) was proposed to solve optimization problems [161].…”

Multiobjective optimization of injection molding using a calibrated predictor based on physical and simulated data

“…Analyses to identify the most significant process variables that influence key quality attributes, such as dimensional and mechanical properties, in IM can be found in Refs. . For instance, Yang and Gao found that packing pressure, melt temperature, and mold temperature are the most significant process variables that affect part weight on an IM plate.…”

Parameter identification of transport PDE/nonlinear ODE cascade model for polymer extrusion with varying die gap

“…Automatic control systems are widely proposed in industrial application to automatically regulate and reject disturbances in processes. [ 9–14 ] EBM can benefit from control systems to automate die gap programming and reduce machine drift. Feedback systems could change the die gap (input) automatically to obtain a desired extrudate thickness (output) or to compensate for any machine drift.…”