Injection molding product weight: Online prediction and control based on a nonlinear principal component regression model

Abstract

Weight is an important quality characteristic of injectionmolding products. The current work focuses on the online prediction and closed-loop control of the product weight. Previous researchers used the process setpoints as the inputs to establish weight prediction model. These models cannot reflect the weight variations at a given setting. In this study, an online weight prediction model has been developed, with the process variable trajectories as the inputs, using a principal component regression (PCR) model. A nonlinear enhancement has been made to improve the prediction accuracy of the PCR weight model. Based on such an online prediction, a closed-loop weight control system has been developed and tested experimentally. POLYM. ENG. SCI., 46:540 -548, 2006.

A systematic optimization approach in the MISO Plastic Injection molding process

“…Gao [10] revealed that product weight is an important quality index for injection-molded products because the product weight has a closer relation to other quality properties (e.g., surface properties and mechanical properties), particularly dimensional properties (e.g., dimensions and thickness). They also claimed that the performance of a manufacturing process and its quality control can be monitored through the product weight.…”

“…Previously, researchers showed that product weight is a critical quality attribute, and a good indicator of manufacturing process stability for PIM [9], [10]. Yang and II.…”

Study on Packing Phase Control Based on the Cavity Pressure-Temperature during Injection Molding

“…There are many quality control methods based on material PVT properties for injection molding process in the literature. Unfortunately, most advanced strategies are difficult to realize in large-scale industrial application due to the high cost of equipment (Dubay et al, 2007;Yi and Furong, 1999;Yi and Furong, 2006).…”

Quality prediction for polypropylene production process based on CLGPR model

“…To date, different data-based soft sensors have been developed for quality prediction purpose, including principal component regression (PCR) and partial least squares (PLS) for linear processes, artificial neural network (ANN) and support vector machine (SVM) for nonlinear processes and etc (Kadlec et al, 2009;Gonzaga et al, 2009;Kano et al, 2008;Yang & Gao, 2006;Gao & Ren, 2010). The polypropylene production process is a well-known highly nonlinear process as evidenced by mechanistic analysis of the reactions and plants (Liu, 2007).…”