Incorporation of Phenomenological Models in a Hybrid Neural Network for Quality Control of Injection Molding

Abstract

Injection molding is characterized by complex dynamics, which makes quality difficult to control. This is because the exact relations among the machine inputs, material properties, and molded part quality are not known precisely. Hence, the existing models for quality prediction have a limited accuracy and difficulty in application to general molding applications. This article investigates the integration of analytical process knowledge and artificial neural networks as a solution for quality prediction of molded parts, with accuracy increased toward quality control targets of three defects per million (60). This article describes the hybrid system based on the neural network and process knowledge, then compares its performances with conventional neural models for the prediction of the injection pressure.

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

“…Furthermore, many recent control theories and techniques have been proposed for designing the temperature controllers of such plastic injection molding processes. Those control approaches can be divided into two technical methodologies 29–39; one is controlled typically by single‐loop controllers and one other by multivariable controllers. The proposed control algorithm was shown to be more powerful than the single‐loop control policy 31 and the multivariable control strategies 32, 35, 37 operating at the slow sampling periods of 10, 20, or 60 seconds.…”

Effect of Different Parameters on WEPS Production and Thermal Behavior Prediction Using Artificial Neural Network (ANN)

“…In this case the quantitative parameter to evaluate WEPS performance is Differential thermal analysis (DTA). A number of authors have investigated the applicability of artificial neural networks (ANNs) to describe polymerization processes (see, for instance, [12][13][14][15][16][17][18] ).…”

Development of an in-process, gap-caused flash monitoring system in injection molding processes

“…The low production costs and high production volume of injection molding processes make it very common in the plastic industry. However, due to the nature of non-linear complexity resulting from the multivariable process, although advantageous, injection molding does not necessarily guarantee superior product quality [2,3]. A number of factors in the injection molding process could cause defects, Z. Zhang · J.C. Chen (u) · Jie Zhu Iowa State University, 221 I. ED.…”

“…The last two categories of defects can be better controlled or eliminated through accurate mold and tooling design and the reduction of variation in material and machine process parameters [1,2,8]. In the 1970s and 1980s, the strategies of mathematical modeling, SPC and SQC were widely used for seeking better injection molding conditions.…”