Over past months, many in-house workflows and development processes have slowed down and also changed. Nevertheless, time has not stood still, also not in injection molding technology. The debate and development have been shaped by two themes: digitalization and the circular economy. Below are some examples of developments and projects that underpin this trend.

Two displays are seamlessly integrated into the continuously 3D-formed instrument panel

Innovative Injection Molding in Times of Pandemic

Spritzgießen

Kunststoffverarbeitung: Verfahren

Schaumspritzgießen

Künstliche Intelligenz für die Qualitätssicherung

Industrie 4.0

Industrie 4.0 für die Qualitätssicherung

Steuern, Regeln

Software

Mehrkomponenten-Spritzgießen

Folientechnik (IMD, IML, FIM, u.a.)

Digitale Transformation

Wirtschaft

Innovationen und neue Technologie

Spritzgießwerkzeuge

Kunststoffverarbeitung: Maschinen und Komponenten

Werkzeuge für die Kunststoffverarbeitung

Biokunststoffe

Kunststoff Werkstoffe

Spritzgieß-Sonderverfahren

Nachhaltigkeit

Umweltmanagement

Spritzgießsimulation

Simulation

Schnecken und Plastifiziersysteme

Spritzgießmaschinen

Industrie 4.0 für die Kunststoffverarbeitung

Machine Learning / Künstliche Intelligenz für die spanende Fertigung

Industrie 4.0 für die spanende Fertigung

Kunststoff-Recycling

Kunststoff-Aufbereitung

Lasermarkieren

Oberflächentechnik

Laserbearbeitungen

Inline- / Online-Messsysteme

Qualitätssicherung

Qualitätssicherung Kunststoff

Kreislaufwirtschaft

There Is More beyond the Horizon

Guido Frohnhaus has been Managing Director Technology & Engineering at Arburg for a good two years now. The pandemic restrictions meant that he has made very few appearances outside the company so far, but, as he says himself, he spent the time getting to know the in-house processes at all levels and in great detail. In this interview with Kunststoffe, the 52-year-old talks about the course he plans to set and how crucial the high level of in-house manufacturing and programming expertise is for the company’s independence.

16:9

Doriat, Clemens, Dr.

“Gestica Will Be the Fixed Star in the Customer’s Universe”

The resource-economical determination of an optimized operating point in injection molding is still a challenge. Machine learning offers great potential for re-using knowledge from a flow simulation that has already been generated.

Significant reduction of the effort for determining the optimum operating point by machine learning

Machine Learning: Combining Simulation and Experimental Test Data

DOE-Software

Qualitätsmethoden

Digitaler Zwilling

Quicker Evaluation of the Optimum Operating Point

The multitude of software applications often requires the handling of information in different data formats. It is not uncommon for information to be lost in the process, which is why PIC 4.0 is researching methods for implementing continuous simulation chains.

Stages of an exemplary product development process for a load-balanced saddle equipped with different tapes

Part 6 of the Series: How a Web-Based Interface Supports Data-Handling

Virtual Reality für die Qualitätssicherung

User-Friendly Data-Exchange in the Digital Engineering

iQ process observer, the new process monitoring platform from Engel monitors hundreds of parameters from the injection molding cell in the background.

The injection molder is truly not a friend of changes – if they occur in the running process. The iQ process observer helps him to keep an eye on the process and thus keep a handle on the quality

A Process Monitoring Platform Monitors Several Hundred Parameters Simultaneously

Steuerungen (Hardware und Software)

Automatisierungstechnik

Prozessvisualisierung

Spritzgießmaschinen, allgemein

Condition Monitoring / Prozessüberwachung

Instandhaltung

IoT-Plattformen für die Kunststoffverarbeitung

An Eye on the Process –  a Handle on Quality

In an online survey, representatives of the plastics industry formulated their expectations toward the Plastics Innovation Center 4.0 (PIC 4.0).

Wordcloud generated from the answers to the open question concerning the most important use cases for PIC 4.0

Part 5 of the Series: Practical Demonstrators for the Plastics Innovation Center 4.0

Messdatenerfassung und -verarbeitung

Prozessoptimierung

Prozessmanagement

Machine Learning

Datenanalyse

Kunststoff-Rezyklat

Focus on the User Benefit

Arburg has adopted a clear message for its trade fair appearance at the K 2022 in Düsseldorf: „There is only a Plan A“.

In the arburgGreenworld pavilion of the VDMA Circular Economy Forum, an Allrounder 370 A with recyclate package is manufacturing “Greenline” wall plugs from Fischer.

Messe K-Kunststoffmesse

kunststoffe-international.com

Energieeffizienz

Sustainability thanks to Digitalization

 At KraussMaffei’s booth, automotive door modules are produced from former caps of insulin pens. The system supplier is presenting not only the machine hardware necessary for this but also new digital solutions.

Digital solutions are an essential component of the material loop from the insulin pen cap to the car door module.

kunststoffe.de

Medizinprodukte

Medizintechnik

From the Insulin Pen Cap to the Automotive Door Module

Cost risks and the loss of knowledge threatened by demographic change, form a scenario for the toolmaking industry for which automated tool design could be a conceivable way out.

Partial automation of the demolding system represents an important step towards complete automation of injection mold design.

Automated Design of the Demolding Systems of Injection Molds

Werkzeugkonstruktion

Dienstleistungen

Werkzeug-, Formen- und Modellbau

Analysetools, FEM

Machine Learning für die Qualitätssicherung

Anticipation of a Larger  System Landscape

The central control unit of the injection-molding machine is used by the machine operator for control, regulation and optimization of the entire production process. In order to directly integrate external systems as well as proprietary solutions that do not have a standardized interface, Engel uses a technology that is already widespread in day-to-day routine. Via a browser, the new Webviewer can be reliably integrated into the CC300 control panel of the injection-molding machines.

The Webviewer in the machine control system integrates not only ancillary equipment  with web interfaces, but also data from internal and external systems that are accessible via browsers. 

New Webviewer Merges Web and Control Applications

Bedienterminals

Computer Hardware

Central Control Unit Makes Use  of Web Technology

Part 2 of the series on the Plastics Innovation Center 4.0 (PIC 4.0) at RWTH Aachen describes the procedure by which industrial use cases of a Smart Factory are identified and converted into real testbeds within the PIC 4.0.

The PIC 4.0 extends the infrastructure of the Institute for Plastics Processing in Industry and Craft at RWTH Aachen University

PIC 4.0 Elaborates Holistic Solutions for Industrial Demands

Use Cases of Digitized Plastics Processing

Maintaining competitiveness while producing in Germany requires not only cost-efficient production, but also high-quality products. Vorwerk Elektrowerke is accepting this challenge. In collaboration with the Institute of Product Engineering at the University of Duisburg-Essen, Germany, the company is testing a 100 % inline quality monitoring system as well as a quality prediction using machine learning.

Customers who choose the Kobold VK 200 vacuum cleaner expect top quality. To ensure this, efforts for the introduction of Industry 4.0 measures are taken in the production plant in Wuppertal, Germany

Successfully Managing Digital Transformation with Automation and Artificial Intelligence

Kameras, intelligente

Fertigungsautomation

Bildverarbeitung

Inline Quality Monitoring and  Data-Based Quality Prediction

Because it has not yet been possible to exploit the full potential of digitization in plastics processing, the Institute for Plastics Processing at RWTH Aachen University is building a new application-oriented research infrastructure along the lines of a Smart Factory. Kunststoffe is accompanying the building of the Plastics Innovation Center 4.0 with a series of publications. Part 1 describes the concept and the initial demands on the physical infrastructure.

Visionary overall picture of the expanded IKV in future, supplemented by the Plastics Innovation Center 4.0

The Plastics Innovation Center 4.0 Is under Construction at the Campus Melaten in Aachen, Germany

Smart Factory

Central Hub for Digitization  in Plastics Processing

Munich-based mechanical engineering group KraussMaffei is repositioning itself and uniting all divisions under a single brand. At a time of accelerated technological change, the company is increasing its efforts to develop new products for digital solutions and new business models – and that includes plastics recycling.

Everything remains different: Dr. Frank Stieler, CEO of KraussMaffei

KraussMaffei Consolidates Three Divisions and the Previous Group Brands under a Single Brand

Extrusion

Ressourcenmanagement

Produktion

Compoundieren

Moving with the Times

As expected, the K 2019 displayed a wide range of machine technology and complete system solutions for plastics recycling. At least since the trade fair in Düsseldorf, Germany, it has become obvious that industry today can offer solutions for closing the plastic cycle.

Complex chain: recycling materials (such as these shredded and washed fishing nets) have to pass through several stations, beginning with collecting and sorting, before they can be mechanically recycled

The Technology to Close the Plastic Cycle Exists

Sensorik für Kunststoff-Verarbeitungsmaschinen

IR-Sensor

Messtechnik

Sensoren / Messwertaufnehmer

Mahlgut

Wiederaufbereitung

Schmelzefilter

Filteranlagen

Extruder

Extrusionsanlagen

Waschen und Entfetten

Sortierer

Mahlanlagen, fein

Sensoren,  optische

Schneidmühlen

Aufbereitungsmaschinen

Mühlen

A Closed Recycling Cycle – Now!

Part 7 of the Series: How the Digital Shadow Enlightens Real Processes in the PIC 4.0

The digital shadow is the core concept of the Cluster of Excellence “Internet of Production” at RWTH Aachen University. It represents the data trace of an asset to describe its real behavior. In the Plastics Innovation Center 4.0 (PIC 4.0), digital shadows, e.g. the data trace of a sensor, are used to optimize processes taking into account real behavior and to determine simulation quality.

Industry and science are constantly striving to fully capture and model production processes in order to manufacture high-quality products in the shortest possible time and with the least possible use of resources. The possibilities of predicting complex interactions between process settings and control variables and influencing product quality are increasing, although predictions are becoming more computationally intensive and automated matching with reality is still very cost-intensive or fails completely. The digital shadow, on the other hand, aims at capturing the real behavior of a process, for example, through the data trace of a sensor, and taking it into account in model calculations [1].

Testbeds Create Use-Case-Related Digital Shadows

The consistent interconnection of machines and peripherals within the PIC 4.0 makes it possible to capture a wide range of real process data. However, in order to integrate scientific methods and approaches seamless into real production environments, these different data traces must be processed and analyzed in a targeted manner. For this purpose, testbeds are set up in the PIC 4.0, which can be used to test the interaction of software services and real testbeds for a specific research question [2]. The initial testbeds focus, among other things, on the influence of the use of recycled material in injection molding production, including the associated material preparation, and, on the other hand, on the synchronization of quality-relevant data traces in order to improve data-driven quality controls and to determine the simulation quality with regard to the real process behavior.

A modular measuring cell is being integrated into an injection molding process for the focused examination of quality control issues. In addition to determining the weight of the molded part, the extent to which optical 3D measurement is currently being examined. This includes CAD output of the real part and measurement of the surface structure using e.g. white-light interferometry and laser scanning methods suitable for this purpose. The individual data traces of these measuring systems are to be supplemented by the data traces of the injection molding machine as well as a fully comprehensive recording and evaluation of cavity pressure and temperature signals. Finally, the nozzle of the injection molding machine is also to be equipped with sensors so that the processes in the screw antechamber can be taken into account for model optimization. The setup of the testbed allows to determine the full potential of the individual measuring systems in the production environment either individually or in combination.

On the software side, there are challenges in data handling as well as in the automated synchronization and assignment of data traces to a production order or even the individual product. On the other hand, new analysis services for the optimization of the process and the prediction models become possible through the interconnection. To this end, a system is being developed which assigns sensor signals directly to a simulated soft sensor in the filling simulation and calculates the real flow-front-velocity for comparison with the simulated ideal at the operator terminal. This will make it possible to implement further assistance systems, e.g. for optimizing the setting parameters to achieve a constant flow-front-velocity as a quality criterion.

Investigations into flow-front-velocity and geometry-dependent process-control are currently the subject of various research projects at the Institute for Plastics Processing (IKV), Aachen, Germany. The planned testbed as a real demonstrator will make the research results available for rapid technology-transfer to industrial applications.

Simulation, Machine and Mold Provide Different Digital Shadows

Particularly in the case of complex-shaped injection molded parts, flow-front-velocities that are too high or too low can occur due to changes in the flow cross-section [3–5]. One approach to prevent quality defects occurring as a result is to ensure a constant flow front velocity by smart adjustments of the injection speed [6–8]. However, it is hardly possible to track the flow-front-velocity even at constant injection speed or even to correlate it with an injection profile. A solution is offered by the filling-simulation, which, however, only corresponds to the real process to a limited extent.

For the implementation of the envisaged assistance system, it is assumed that the filling simulation maps the basic filling behavior and the flow path sufficiently accurate. The fine tuning of the simulated flow-front-velocity to the real process is achieved by correlating the data traces from the machine, sensors and simulation. An operator interface near the injection molding machine should make it possible to intuitively assign the simulated sensor positions to real sensor signals. In addition, the correlation between sensor signals and a defined filling level is ensured by this interconnection. The simulation software, the mold sensor system and also the injection molding machine itself provide data traces with different information content for this (Fig. 1).

The digital shadow of the machine provides insight into the real acceleration of the screw and the volume flow induced by it. This acceleration behavior and the non-ideal volume flow and pressure transfer from the plasticizing-unit are the main reasons for the deviations between simulation and reality. The closing behavior of the non-return valve and the volume-loss that flows back via the screw flights also play a significant role in the fact that, measured against the screw position, a deviating melt volume must be dosed and injected. Even with knowledge of a simulated filling process and the virtually optimized process point, it is difficult to determine the correct parameter setting on the injection molding machine.

In addition to the marked switchover point (Fig. 1 top left) at 1.368 s for volumetric filling of the product, four sensor positions are highlighted. Four piezoelectric combined temperature and pressure sensors are installed inside the cavity, providing insight into the real filling behavior of the product. Due to the different acquisition systems, e.g. directly via the injection molding machine or via a separate edge-device, the difficulty of how to synchronize the measurement records stands out. If the flow front reaches a sensor, this is very clearly recognizable by the temperature deflection, as is the abrupt drop in temperature at sensor signal 4 at the time of the switchover point (Fig. 1 bottom left). This also allows the other sensor signals to be clearly assigned, so that the relevant measuring time of the sensors in the digital shadow of the injection molding system can be directly correlated with the filling time.

The third figure (Fig. 1 right) shows a simulation of the filling process corresponding to the process point. The sensor positions can be positioned analogously to the real mold in order to match simulation and real process. Based on the results of the process simulation, the flow front can be traced and the average flow-front-velocity can be calculated for each filling level. For this purpose, the time of filling, filling level, pressure drop and melt velocity are simulated for each node of the simulation mesh. Due to the idealized system assumptions made when setting up the process simulation and neglecting the machine-related influences, the filling time as well as the times at which the nodes of the sensor positions are reached deviate from reality.

Finally, the correlation of the individual data traces makes it possible to match simulation and reality (Fig. 2). For this purpose, both the simulated flow-front-velocity is adjusted to the real filling and the time intervals between the respective simulated and realized sensor signals, at which the melt reaches the sensors.

The assistance system will enable the operator to fine-tune the injection profile directly at the machine, taking into account the product geometry. At the same time, the system will enable researchers within the PIC 4.0 to evaluate this and other optimization approaches for industrial application in a data-based manner by combining digital shadows, for example by adding data traces on the melt pressure in the screw antechamber or by taking into account the automated comparison between realized and simulated product quality through integrated 3D measurement of the product. This makes the PIC 4.0 a demonstration platform for innovative optimization services from research concept to industrial application.

[1] Becker, F.; Bibow, P.; Dalibor, M.; Gannouni, A.; Hahn, V.; Hopmann, C.; Jarke, M.; Koren, I.; Kröger, M.; Theissen-Lipp, J.; Maibaum, J.; Michael, J.; Rumpe, B.; Sapel, P.; Schäfer, N.; Schmitz, G.; Schuh, G.; Wortmann, A.: A Conceptual Model for Digital Shadows in Industry and its Application. Proceedings of 40th International Conference on Conceptual Modeling, 2021

[2] Hopmann, C.; Schmitz, M.; Bibow, P.: The PIC 4.0 as Part of the Internet of Production. Kunststoffe International (2021) 2, pp. 7–9

[3] Bichler, M.: Prozessgrößen beim Spritzgießen – Analyse und Optimierung. Beuth Verlag, Berlin, Vienna, Zurich 2012

[4] Chen, X.: A Study on Profile Setting of Injection Molding. Dissertation, Hongkong University of Science and Technology 2002

[5] Johannaber, F.; Menges, G.: Injection molding machines. A user’s guide. Hanser; Hanser Gardner, Munich, Cincinnati 2008 (ISBN: 978–3–446–45011–0)

[6] Gruber, J.-M.: Process control based in cavity pressure for the injection moulding process of thermoplastics. Dissertation, RWTH Aachen University, Aachen 2005 (ISBN: 3–86130–843–6)

[7] Michaeli, W.; Bourdon, K.; Haupt, M.; Hunold, D.; Robers, T. F.: Das Volumenstromübertragungsverhalten in der Einspritzphase. Plastverarbeiter 41 (1990) 9, pp. 186–195

[8] Yang, Y.; Chen, X.; Lu, N.; Gao, F.: Injection molding process control, monitoring, and optimization. Hanser Publications; Hanser Publishers, Cincinnati, Munich 2016 (ISBN: 1569905932)

Institut für Kunststoffverarbeitung IKV in Industrie und Handwerk an der RWTH Aachen

https://www.ikv-aachen.de

Ausgabenartikel

The construction of the PIC 4.0 proceeds and throws its shadow on the Campus Melaten.

Fig. 1. Data traces of the injection molding machine about the screw acceleration, from simulation about the flow-front-velocity and from sensors about the real filling-behavior exemplary for a honeycomb-structured part.

Fig. 2. The interconnection of data traces from simulation and machine enables further extension of optimization strategies with respect to injection profiles at an injection molding machine. 

Name - Hauptartikel

The Cluster of Excellence Internet of Production (IoP) is an interdisciplinary research project funded by the German Research Foundation (DFG). More than 25 institutes and further institutions near the university cooperate in the first funding period (01/2019 – 12/2025) on topics of digitized production technology to enable sustainable competitiveness of the German industry. The future technologies of the IoP should result in real demonstrators inside the PIC 4.0.

<b>Univ.-Prof. Dr.-Ing. Christian Hopmann</b> has held the Chair of Plastics Processing as Head of the Institute for Plastics Processing (IVK) in Industry and Craft at RWTH Aachen University, Germany, since 2011.

<b>Pascal Bibow</b>, M.Sc. RWTH, has been working since September 2016 on intelligent production systems at IKV. Since January 2020, he has been Managing Director of the Plastics Innovation Center 4.0;

<a href="mailto:pascal.bibow@ikv.rwth-aachen.de">pascal.bibow@ikv.rwth-aachen.de</a>

The construction of the Plastics Innovation Center 4.0 is funded by the State of North Rhine-Westphalia and by the European Regional Development Fund (ERDF).

The following contribution describes how flexible structures enable a versatile research environment and thereby a continuous innovation management. It appears in one of the next issues.