Sebastian Stemmler

A self-optimising injection moulding process with model-based control system parameterisation

The consideration of the pvT-behaviour (pressure, specific volume and temperature) of the plastic material in combination with closed-loop cavity pressure control allows for compensation of variable boundary conditions in the injection moulding process. By suitably implementing cavity pressure control, repeatability and product quality in injection moulding processes can be improved. However, there are still obstacles for industrial application. As the process behaviour is greatly dependent on the mould – which is interchangeable and typically designed and manufactured independently of the machine’s control system – challenges arise in designing a cavity pressure controller that yields high performance and at the same time is robust enough to be suitable for universal use. The use of a model predictive controller (MPC) for cavity pressure control is being researched and found to be helpful to overcome these issues. Unlike controllers such as proportional-integral-derivative controllers, the control output is not determined using a well-tuned, but mathematically relatively simple algorithm. Instead, it performs an online optimisation based on a process model in order to obtain the control outputs. In order to operate as intended, the model used by the MPC has to be adjusted with every significant change of the system, in particular the machine, material and mould. Therefore, a process model as well as a suitable strategy for in-process identification of the necessary parameters is developed and presented. For automated parameterisation, a strategy based on two experiments is suggested and first experimental results are presented.

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.

Model Predictive Control of Cavity Pressure in an Injection Moulding Process

Abstract Cavity pressure control is a means of improving repeatability and product quality in injection moulding processes. As the system behaviour is greatly dependent on the mould - which is interchangeable and typically designed and manufactured independently of the machines control system - challenges arise in designing a controller that yields high performance and robustness to be suitable for universal use. A cavity pressure controller intended to be used for a wide variety of moulds will likely need some form of reparametrisation. In order to gain user acceptance, the process of manual or automatic parametrisation of the controller to a new mould needs to be simple enough to be performed and understood by staff that are not necessarily control experts. Addressing this issue, the authors suggest an approach using a Model Predictive Controller that is based on a physically motivated grey-box model. The model is simple enough to be intuitively checked for plausibility but sophisticated enough to reproduce the dominant behaviour of the system. For automated parametrisation, a strategy based on two experiments is suggested. The experiments are tailored to be suitable for incorporation into the regular production process. The concept is presented and first experimental results are shown.

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.

Modellbasierte prädiktive Kraftregelung beim Fräsen

Kurzfassung Die Zerspankraft liefert in spanenden Fertigungsverfahren eine wichtige Information über den Prozesszustand. Trotz optimal ausgelegter Prozesse wird selten eine konstante Zerspankraft erreicht, da Unsicherheiten, wie z. B. Werkzeugverschleiß oder Materialkenngrößen, diese beeinflussen. Aus Schutz vor Überlast, werden die Prozessparameter so gewählt, dass die vom Werkzeug ertragbare Last auch zum Standzeitende nicht überschritten wird. Damit einher geht ein Produktivitätsverlust bei schneidscharfem Werkzeug. Durch Regelung der Prozesskraft kann eine signifikante Produktivitätssteigerung erreicht werden. Die modellbasierte prädiktive Regelung (MPR) erlaubt die explizite Berücksichtigung von Nichtlinearitäten sowie zeitvariantem Übertragungsverhalten und ermöglicht so eine deutlich höhere Regelgüte als klassische PI-Regler.

Model-based predictive force control in milling: determination of reference trajectory

Today, powerful process simulation tools allow an offline process planning and optimization of metal cutting processes. The quality of the optimization strongly depends on the model and its parameters. Real cutting processes are influenced by uncertainties such as tool wear status or material properties, which are both unknown. To overcome this limitation, sensors and process control systems are used. Model-based Predictive Control (MPC) was developed in the 1970s for the chemical process industry. This control method was found to be very suitable to control complex manufacturing processes such as milling processes. Using MPC in metal cutting processes allows considering technological boundary conditions explicitly. Adapting the feed velocity and thus the process force increases the productivity and process stability in milling. A core element of the MPC is the use of a reference trajectory representing the time-dependent set point value in the optimization procedure. The tool path information, however, is given position-based. Thus, calculating the reference trajectory is not trivial and strongly influences the control quality. This paper presents two methods for determining the reference trajectory. The first method is based on an adaptive signal filter. For the second method the MPC is extended to a two-layer MPC: the first layer calculates an optimal reference trajectory; the second layer controls the machine tool.

Approaches of Self-optimising Systems in Manufacturing

Within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” one major focus is the research and development of self-optimising systems for manufacturing processes. Self-optimising systems with their ability to analyse data, to model processes and to take decisions offer an approach to master processes without explicit control functions. After a brief introduction, two approaches of self-optimising strategies are presented. The first example demonstrates the autonomous generation of technology models for a milling operation. Process knowledge is a key factor in manufacturing and is also an integral part of the self-optimisation approach. In this context, process knowledge in a machine readable format is required in order to provide the self-optimising manufacturing systems a basis for decision making and optimisation strategies. The second example shows a model based self-optimised injection moulding manufacturing system. To compensate process fluctuations and guarantee a constant part quality the manufactured products, the self-optimising approach uses a model, which describes the pvT-behaviour and controls the injection process by a determination of the process optimised trajectory of temperature and pressure in the mould.