Julian Heinisch

Self-optimizing injection molding based on iterative learning cavity pressure control

Modern injection molding machines can reproduce machine values, such as the position and speed of the plasticizing screw, with a high precision. To achieve a further improvement of the part quality, adaption and self-optimization strategies are required, which is realized by the implementation of a model-based self-optimization to an injection molding machine. Within this concept, a pvT-optimization allows an online control of the holding pressure that is tailored to the plastics material, considering the relationship between pressure, specific volume and temperature. A control strategy is required that controls the cavity pressure with respect to the reference generated by the pvT-optimization. However, cavity pressure control, in contrast to pressure control in the plasticizing unit, is hitherto not possible without a time-consuming system parametrization. Due to the repetitive character of the injection molding process, the iterative learning control (ILC) is a suitable approach. The ILC uses information gained within the previous cycle and a model to generate the optimal controller outputs for the following cycle. Based on this iterative learning, the reference tracking of the cavity pressure can be improved over several cycles. Additionally, repetitive disturbances can be compensated automatically. To improve the convergence speed of the ILC, a process model can be used explicitly. Based on this premise, an ILC for cavity pressure control is developed and researched in injection molding experiments. It is shown that the flexibility of the control strategy can be improved without compromising performance.

Self-optimizing Production Technologies

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Influence on Product Quality by pvT-Optimised Processing in Injection Compression Molding

Abstract The production of functional optical parts made of plastics is a growing market and the associated production processes have to be developed to fulfil the increasing quality requirements. In this paper an alternative injection compression molding process using the pvT-behavior of the plastics material is compared to the conventional processing method using an isobaric process path. For both control strategies a cavity pressure loop-control is implemented and used. According to measurements of the mold surface reproduction accuracy and internal properties of the molded part the processing method using the pvT-behavior results in a higher optical part quality in the same cycle time compared to the isobaric processing method.

Process control strategies for injection molding processes with changing raw material viscosity

Abstract The ultimate goal of process control in injection molding is to achieve a reproducible part quality despite changing boundary conditions and disturbances. Especially, changing material viscosities have a considerable impact on part quality and are often inevitable. This study starts with a brief review of the state of the art in injection molding control strategies with regards to melt viscosity. The requirement for a process control strategy with a direct feedback to part quality adapting to the current process viscosity and using in-mold data is derived. In this paper, a fuzzy controller using a pressure loss measurement is implemented for a cycle-to-cycle control of injection velocity based on the current melt viscosity. Depending on the conditions, the controller enables to realize a set point jump in viscosity after approximately six injection molding cycles. To improve control performance and to realize an online closed-loop control in the injection phase, the system behavior of a mold using pressure sensors in the hot runner manifold to obtain a viscosity equivalent indicator is analyzed. The mold in combination with a fuzzy controller using injection velocity and hot runner temperature as actuating variables provide a sound basis for viscosity control in injection molding.