Elsevier

Control Engineering Practice

Volume 16, Issue 11, November 2008, Pages 1259-1264
Control Engineering Practice

Barrel temperature control during operation transition in injection molding

https://doi.org/10.1016/j.conengprac.2008.02.003Get rights and content

Abstract

Injection molding barrel temperatures have slow responses. During the transitions between the machine's idle state and the operation state, significant temperature variations exist in the temperature zones. This leads to inhomogeneous melt temperatures and inconsistent product quality. This paper introduces this industrial problem and then presents a combined strategy using a feedback controller and an iterative learning feedforward (ILFF) controller to resolve the problem. The proposed strategy is implemented and tested on an industrial-sized reciprocating-screw injection molding machine. Experiments show that the combined control strategy has superior performance compared with that of the traditional feedback controller.

Introduction

As a key polymer processing system, injection molding produces diverse products for industrial and domestic applications. With increasing demands for better quality products, control of the key process variables in the injection molding process, such as the barrel temperature, has become a challenge.

In the past, the barrel temperature was typically controlled by a standalone proportional-integral-derivative (PID) controller in each individual zone (Gomes, Patterson, & Kamal, 1986). PID controller provided good robustness at the cost of poor transient performance and interactions among different zones. This led to significant temperature overshoots and oscillations during start-up and operation transitions. Large temperature overshoots and oscillations can cause polymer degradation particularly with thermally sensitive materials. Recently, efforts have been focused on controlling the barrel temperature with advanced control methods, such as self-tuning predictive strategies (Tan & Hofer, 1995; Tsai & Lu, 1998), self-optimizing model predictive control (Dubay, 2002), multivariable model predictive control (Dubay, Bellc, & Gupta, 1997), variable structure control (VSC) (Su & Tsai, 2001), and state-space control systems (Bulgrin & Richards, 1995).

Compared with classical control laws (such as PID), predictive control laws are very powerful for processes with large dead-time or for processes with predetermined set-point (Tan & Hofer, 1995). A multivariable model predictive controller has been developed to handle the multi-input–multi-output and the interaction nature of the process. It has been shown to be effective in the extrusion process (Dubay et al., 1997).

In the injection molding industry, on-line set-point changing, particularly with the barrel temperature, happens rarely. Consequently, in addition to the control of the barrel temperature at a given set-point, the industry is more concerned with the following two issues, particularly when molding with expensive materials:

  • (a)

    Start-up period control. It is desirable to achieve as short as possible start-up while preventing temperature overshoots.

  • (b)

    Consistency of barrel temperature control. During the transitions between the idle state and operation, it is desirable to have the barrel temperature controlled as consist as possible to guarantee the final product quality consistency.

Despite of those numerous applications of advanced controls for injection molding, none discussed and focused on these two issues. The barrel temperature start-up control had been resolved in our work (Yao & Gao, 2007) via time optimal control (TOC). This paper focuses on the temperature variation problem during the transitions between the idle state and operation to prevent large temperature variations. The remainder of this paper is so organized: In Section 2, the process is introduced and the problem is formulated in detail. In Section 3, the method used in this work is described. Section 4 presents the experimental results while the conclusions are presented in Section 5.

Section snippets

Process description and problem formulation

Injection molding is a well-established technique used widely in the polymer processing industry. The injection molding process that occurs in a reciprocating-screw injection molding machine converts raw plastic materials (usually in pellet form) into products of different sizes and shapes. A typical machine, shown schematically in Fig. 1, consists of four major units: the injection unit, the clamping unit, the hydraulic unit, and the control unit (Yang & Gao, 2000). Prior to the molding

Feedback GPC

The feedback method used here is GPC, initially proposed by Clarke, Mohtadi, and Tuffs (1987). It has become a widely adopted method in various industries. A brief introduction is given below and detailed derivation of the algorithm can be found in Clarke et al. (1987). The basic idea of GPC is to determine an “optimal” sequence of future control moves by minimizing a multistage cost function defined over a prediction horizon. The index to be minimized is typically the weighted sum of two

Experimental results

All experiments were conducted on a Chen Hsong reciprocating-screw injection-molding machine (model JM88MKIII), with the maximum shot weight of 128 g and clamping force of 88 tons. The barrel is divided into seven zones including one nozzle zone and six barrel zones numbered 1–6 from the nozzle to the hopper. Each zone has one independent heater. The nozzle zone heater has a capacity of 600 W. The other six heaters have 1080 W capacities. All the temperatures are measured by a K-type thermocouple.

Conclusions

The transitions between the machine idle state and operation state result in large barrel temperature variations in the injection-molding process. A combined strategy of feedback (GPC) control and ILFF control has been designed to solve this problem. Experimental results on an industrial-grade machine clearly show the effectiveness of the proposed strategy. The developed control strategy can maintain the barrel temperatures tightly around the set-point during normal operation, machine idle and

Acknowledgment

This project was supported, in part, by the Hong Kong Research Grant Council by project 613107 and by the Hong Kong/Germany Joint Research Scheme by project G-HK025/02.

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