Self-optimizing Injection Molding

 

Production of injection molded parts requires robust and consistent operating processes to satisfy high quality standards. The goal of such processing is to reproduce each cycle identically and consistently within several process variables. In particular, process variables such as the melt pressure and temperatures in the cavity. Conventional controlling of injection moulding machine controls is based on recognized variables, which influence the reproducibility of molding processes by additional process disturbances. During the packing phase for example, temperature or viscosity fluctuations may affect the reproducibility of the process. The concept of self-optimizing injection molding therefore, is to compensate for reoccurring process variations and increase reproducibility.

 

Practical Issues

Injection Molding CoE

Machine variables can be referred to as values which describe the state and action of an injection molding
machine. The hydraulic pressure of the injection cylinder is such a parameter. Term process variables summarize the variables, allowing for a clearer description of the process at the actual location where the molding takes place. The cavity pressure is another example of such a process variable. Properties of manufactured molded
parts which result from injection moulding can be described as quality variables, and an example of quality variables are the part weight and geometry accuracy. Due to disturbances acting upon the injection molding process, exclusive control of machine variables are insufficient to reproduce the process, but cannot ensure consistant molding quality. Therefore, the monitoring or direct control of process variables is desired. Systematic disturbances such as a fluctuating ambient temperature, or varying material properties can seriously affect product
quality. This includes the changes in the heat balance of the injection mold which can occur for example, with a
non-identical repetitive process such as after changing machine parameters. In contrast to the machine variables
(such as the hydraulic pressure), process variables (such as the cavity pressure) provide detailed information about
processes, during the injection and holding pressure phase. The cavity pressure path correlates with various quality
variables such as the part weight, or molding accuracy, warpage or shrinkage, as well as morphology and sink marks.

 

Approach

Compensation of thermal fluctuations by self-optimizing in injection molding Cluster of Excellence Integrated Production Technology for High-Wage Countries Compensation of thermal fluctuations by self-optimizing in injection molding

The concept of a self-optimizing system is divided into model based optimization (MO-System) and various information processing sensor actuator systems (ISA-Systems). Temperature sensors, like IR-Sensors or thermoelements are the thermal boundary conditions which can be measured and a working point determined by
the ISA-System. A working point is used to calculate the optimal processing path of the cavity pressure based on the quality model. This optimal cavity pressure path will describe the internal objective for another ISA System, which then realizes its internal objectives, such as reaching the cavity pressure path independently. During the first phase of the Cluster of Excellence research, the concept of a self-optimizing injection molding process was to compensate for temperature fluctuations and ensure consistent quality was applied even with changing boundary conditions or disturbances. As an example of changing boundary conditions, the failure of two heating zones of a plasticizing unit was simulated, by switching off the heating zones and observing the effect on the part weight. The effect was observed using a conventional machine control with a fixed holding pressure path, and a comparison of the parts produced, using the concept of self-optimizing injection molding. The figure highlights the results of the experiment where a plate made of polypropylene was used. The effect of the weight change on the basis of the changing molding temperature was significantly lower when using the selfoptimizing concept.

 

Technical Challenge

Combined process optimisation Cluster of Excellence Integrated Production Technology for High-Wage Countries

Several initial challenges will be made possible as result of technological, electronic advances in injection molding machines. For one, the robustness and self-adaptation ability of the cavity pressure control will be tested and validated. The self-adaptation of the cavity pressure control will prove essential in later practical feasibility tests of the self-optimizing injection molding process, and this will include an autonomous parameterization of the controller. Furthermore, the controller architecture will be selected autonomously from different controller architectures differing in their complexity. Similarly, the current concept of the self-optimization injection molding will be extended by cross-cycle optimizations. The combination of online control and cross-cycle optimization will be necessary to compensate for the heat balance fluctuations after changing machine parameters. The compensation of the thermal fluctuations will be accomplished with the application of the previous concept (approach) of the self-optimizing injection molding machine. Meanwhile, cross-cycle optimizations will put a special focus on compensation of material variations, such as the changing viscosity properties of processed plastic.