In production and research, process data and previously defined quality values are tracked to determine optimization models and pursue consistent quality management. In contrast, data that is not relevant to the specific application is rarely recorded.

Structure and interconnection along the information-pyramid subsequently generate knowledge from sensorsignals.

16:9

Part 9: Consistent Data Structures for Sustainable Access to Secure Knowledge

Spritzgießen

Kunststoffverarbeitung: Verfahren

Messdatenerfassung und -verarbeitung

Software

Qualitätsmanagement

Machine Learning

Datenanalyse

Datenbanken

Industrie 4.0 für die Kunststoffverarbeitung

Industrie 4.0

Big Data für die Qualitätssicherung

Industrie 4.0 für die Qualitätssicherung

Qualitätssicherung Kunststoff

Qualitätssicherung

IoT-Plattformen für die Kunststoffverarbeitung

Complete Data without Ifs and Buts

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

Künstliche Intelligenz für die Qualitätssicherung

Mehrkomponenten-Spritzgießen

Digitale Transformation

Wirtschaft

Innovationen und neue Technologie

Kameras, intelligente

Fertigungsautomation

Bildverarbeitung

Machine Learning / Künstliche Intelligenz für die spanende Fertigung

Industrie 4.0 für die spanende Fertigung

Inline- / Online-Messsysteme

Inline Quality Monitoring and  Data-Based Quality Prediction

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

Schaumspritzgießen

Steuern, Regeln

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

Spritzgießwerkzeuge

Kunststoffverarbeitung: Maschinen und Komponenten

Werkzeuge für die Kunststoffverarbeitung

Biokunststoffe

Kunststoff Werkstoffe

Spritzgieß-Sonderverfahren

Nachhaltigkeit

Umweltmanagement

Spritzgießsimulation

Simulation

Schnecken und Plastifiziersysteme

Spritzgießmaschinen

Kunststoff-Recycling

Kunststoff-Aufbereitung

Lasermarkieren

Oberflächentechnik

Laserbearbeitungen

Kreislaufwirtschaft

There Is More beyond the Horizon

Time to take stock: in what condition and with what agenda is the industry presenting itself after two-and-a-half years of a difficult climate? We have conducted a survey: insights into the dominant themes of the age.

Preview of K 2022: Kunststoffe Questionnaire on the Situation in Injection Molding Machine Construction

Steuerungen (Hardware und Software)

Automatisierungstechnik

Doriat, Clemens, Dr.

Messe K-Kunststoffmesse

kunststoffe-international.com

Mess-, Steuer- und Regelgeräte

Energieeffizienz

Kunststoff-Rezyklat

Harsh Realities

After the focus was on individual manufacturing and model-supported process set-up in recent years, the Institute for Plastics Processing (IKV) at RWTH Aachen University is linking the two aspects of digitalization and circular economy at K 2022.

The QR code on the PCR component provides project information and process data.

Werkzeuginnendrucksensoren

Messtechnik

Sensoren / Messwertaufnehmer

kunststoffe.de

Precise Process Control with Injectix

The latest trend in digitization  is the Artificial Intelligence of Things. It amounts to “marrying” Artificial Intelligence with the Internet of Things. In the following interview, Martin Dimmler and Martin Rugfelt discuss how companies can profit from AIoT and what plastics manufacturers can learn from its pioneers in other industries.

Where Artificial Intelligence of Things (AIoT) is used, value-adding opportunities for more efficiency, quality, and thus higher sales can be realized in every industry

Artificial Intelligence of Things: Trends and Models for the Plastics Industry

Vernetzung / Cloudlösung für die Kunststoffverarbeitung

IoT-Plattformen für die Qualitätssicherung

Fernwartung / Ferndiagnose

Instandhaltung

Produkt- / anbieterspezifische Automatisierung

Automatisierungszellen

“Intelligent Automation Brings Systems Closer to their Optimum”

Paint bucket or A-pillar finisher? In our thoughts and attribution of value, packaging items and high-tech components often represent opposites. Yet the two can complement one another – when you look at used packaging as a valuable raw material. At the fair, KraussMaffei will be showing the concept for a closed material cycle and is making plastics processing sustainable through this.

First a bucket or another plastic product, then raw material for further processing: the shredded post-industrial material

At the K, KraussMaffei Will Be Showing the Cycle from Product to Upcycling  to a New Product

Smart Factory

Laserbeschriftung

Rückverfolgbarkeit, Traceability

Handling, Handhabungstechnik

Zerkleinerungsmaschinen

Greifer für die Kunststofftechnik

Compoundieren

Today Packaging,  Tomorrow Raw Material

Innovation management affects many areas, which now increasingly include digital services in addition to classic product and process innovation. Beyond the continuous reassessment of technical setups, a platform is being established for the consistent provision and further development of software services and dedicated analysis algorithms. Thereby, services are being provided to generate new ideas from the application and to facilitate the transfer of technology into industrial practice.

The outer skin of the PIC 4.0 is closing, inside the design of the demonstration cells is in full swing.

Part 8 of the PIC 4.0 Series: Flexible Infrastructures Facilitate Access to Innovative Services

Innovationsmanagement

Produktentwicklung (F+E)

Smart Services

Internet der Dinge

Messdatenerfassung, Software

Softwarearchitektur

Adaptable Structures for  More Innovation

Unforeseen downtimes in serial production are always accompanied by high costs, making it essential for machines to be serviced in time. Because sensor, transmission and data storage technology have taken a big leap forward in recent years, predictive maintenance has already been realized in some industries, demonstrating a tangible benefit of Industry 4.0. The WZL and the WBA have developed a guideline for this.

Fig. 1. The guideline for the development of predictive maintenance solutions is divided into three phases: analysis, design and implementation

Guideline to the Development of Predictive Maintenance Systems for Serial Producers and Toolmaking Companies

Condition Monitoring / Prozessüberwachung

Werkzeug- und Formenbau

Dienstleistungen

Werkzeug-, Formen- und Modellbau

Sensoren verschiedener Messverfahren

Sensorik / Aktorik

Dienstleistungsqualität

Instandhaltungsmanagement (TPM)

Vorbeugende Instandhaltung für die Qualitätssicherung

Werkzeugbau

Machine Learning für die Qualitätssicherung

Symbiosis of Toolmaking and  Serial Production

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

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.

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

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.

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

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

Sensorik für Kunststoff-Verarbeitungsmaschinen

Digitaler Zwilling

Out of the Shadow into the Light

 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.

Medizinprodukte

Medizintechnik

From the Insulin Pen Cap to the Automotive Door Module

The IKV is researching digital solutions for better description and classification of recyclates. The aim is to increase the actual recyclate use rates. Results will be presented at the IKV colloquium in September.

PlasticBOND: Schematic illustration of the platform design to be developed.

Research to Improve Recyclate Use

The toolmaking industry of the future is facing significant changes in its range of services and its value creation processes. It is difficult to predict exactly how the collaboration of German tool manufacturers with series production will be structured in the future. However, it can be assumed that digital networking and sustainability will play a key role.

What the Two Trend Topics Mean for Collaboration between Toolmaking and Series Production

Ressourcenmanagement

Produktion

Toolmaking of the Future –  Digitalized and Sustainable

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

In an online survey, representatives of the plastics industry formulated their expectations toward the Plastics Innovation Center 4.0 (PIC 4.0). Beside better possibilities to transfer the research results into industrial practice, the respondents expect progress in the area of quality assurance, process optimization, simulation and the linking of process chains. In order to explore these issues, representative demonstrators are being set up within the new PIC 4.0 pilot plant.

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

With the Plastics Innovation Center 4.0 project, the Institute for Plastics Processing (IKV) at RWTH Aachen University, Germany, is erecting a technology and demonstration platform for plastics processing companies that offers solutions to urgent and relevant issues in industrial production. To support adapting the research environment to the needs of the plastics industry in the best possible way, 44 industry representatives prioritized the most important application fields in an online survey between October 2020 and January 2021.

As indicated by the response to an open-ended question about their expectations, respondents expect PIC 4.0 to have a strong practical focus and offer solutions that link multiple process steps to improve traceability. Data based process optimization is also a particular focus of interest. In addition, some respondents expect the development and application of Industry 4.0 standards or the investigation of a certain special process.

In the further course of the survey, the participants first qualitatively evaluated 23 application fields (use cases) with regard to their relevance. Finally, they highlighted the five most important application fields by awarding one to five points (Fig. 1).

Inline Data Acquisition for Better Process and Part Quality

From the survey, inline quality assurance emerges as the most important application field for the use of digital methods. Quality assurance relates closely to the, also highly rated, application field of setup refinement and optimization: an unstable process generally leads to insufficient part quality. It is well known that, particularly in injection molding, controlling only the machine parameters cannot compensate for fluctuations in raw material properties or ambient conditions.

Advanced adaptive control concepts, such as the pvT control developed at the IKV <a href="http://[1]">[1]</a>, allow a variance reduction compared to the traditional control of only the machine parameters; the part quality can however not be inferred directly. The same applies to commercially available products such as APC plus by KraussMaffei <a href="http://[2]">[2]</a> and iQ weight control by Engel <a href="http://[3]">[3]</a>, which recognize material-related viscosity fluctuations by a change in the injection pressure profile and compensate for them by adjusting the switchover point. Since they do not measure the actual part quality, these control concepts are as good as the previously defined reference curve at most.

Similarly, when using unsupervised machine learning for anomaly detection, a typical process trajectory does not automatically indicate a good process. However, an inspection of all manufactured parts is usually too expensive. When inspecting random samples only, the inspection result usually cannot be linked to the exact manufacturing cycle. This circumstance also complicates establishing AI-based quality prediction models, as their training requires a large amount of process data annotated with the part quality. Therefore, an inspection of all parts is usually necessary at least for a certain period of time <a href="http://[4]">[4]</a>.

To alleviate the quality dilemma, the PIC 4.0 pilot plant will feature a modular inspection cell for injection molded parts. A defined connection between injection molding machine and part inspection through a handling system simplifies and automates assigning the quality data to the process data and machine parameters. As a result, quality data recorded through different measurement technologies (e. g. imaging test methods, scales) is directly integrated into the cyber-physical production system. Annotated data sets that can be used to train predictive models or develop new control concepts are consequently available from a central database.

Furthermore, as demonstrated by the IKV at the K 2019 <a href="http://[5]">[5]</a>, such a setup can significantly accelerate the process setup during injection molding, too: combined with simulations, the direct feedback on part weight and warpage made it possible to learn a predictive model for part quality as a function of the setup parameters with reduced efforts. An optimization calculation based on the learned model delivered a functioning working point after only a few tests.

Many respondents expect an improved linking of data generated along the process chain. This expectation is also reflected in the high rating of the application field feedback of process data.

Not only the molding process, but also the composition and molecular weight of the material as well as its moisture content and thermo-mechanic history influence the properties of the molding [6, 7]. Particularly due to the increasing importance of circular economy and therefore recyclates, it becomes important to pass on data on material properties and processing history along the value chain and to correlate them with the resulting process and quality data. Provided significant correlations exist, fluctuations in material quality could eventually be compensated along the processing chain, thereby making the use of recyclates possible and economically viable in further fields of application.

The recycling of PET bottles provides a suitable complexity to represent an interlinked process chain on a laboratory scale; it therefore provides the basis for a demonstrator for closed material cycles (Fig. 2).

Standardized data acquisition via OPC UA, and a central semantic data repository make it possible to link the quality data of the part with the process parameters and measured values from all upstream processing steps. For example, the mechanical energy input and thermal history of the material can be monitored throughout the entire processing chain in order to optimize processing.

Taking into account the susceptibility to damage and processing history of the material, production companies can consequently determine the most economically and technically sensible processing steps as well as an optimal process point for each step. A suitable material and processing model may for instance anticipate whether pre-damaged material, although withstanding the preforming process, will fail during blow molding.

Many respondents wish to see practical examples of digitization solutions and better possibilities to assess their benefits. To this end, PIC 4.0 implements digital services for shop floor and production planning, which IKV employees, especially the ones not explicitly working on digitization topics in their own research, will use on a day-to-day basis. Specifically, an assistance system for carrying out experiments and a system for booking machines and equipment, which can later also serve as the basis for automated production planning, are being developed.

On one hand, these practical services help digitization researchers at the IKV to explore questions of ergonomics and usability; on the other hand, industrial customers can experience these demo applications in the research lab for themselves. Together with the demonstrators for quality assurance and circular economy described above, the Plastics Innovation Center 4.0 will thus offer a tangible experience of the possibilities of digitized plastics processing.

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

https://www.ikv-aachen.de

Ausgabenartikel

Fig. 2. PET bottle recycling constitutes a suitable use case for a demonstrator for closed material cycles

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

Fig. 1. Survey result (excerpt): how relevant do you consider the listed use cases (left)? Which use cases would you like to highlight as particularly relevant (right)?

Name - Hauptartikel

A detailed presentation of all survey results can be found at

<a href="https://share.ikv.rwth-aachen.de" target="_blank">https://share.ikv.rwth-aachen.de</a>/s/SinCdYGiXBBMdp2#pdfviewer

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

Pascal Bibow, M.Sc., is managing director of the Plastics Innovation Center 4.0.

Judith Weber, M.Sc., develops smart production systems at the IKV. Within PIC 4.0, she is responsible for use case development and testbed definition;  <a href="mailto:judith.weber@ikv.rwth-aachen.de">judith.weber@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).

In addition to the physical testbeds in the pilot plant, PIC 4.0 also explores the virtual process chain in digital engineering, which has emerged as the second most important use case from the survey. In one of the next issues, the authors report on the tools of digital engineering to ensure consistent data transfer between production and development. The previous parts have been published in issues 5/2020, 7/2020, 10/2020, and 2/2021.

<ol><li>Michaeli, W.; Schreiber, A.: Der Innendruck im Fokus der Regelung. Plastverarbeiter 11/2008, pp. 30–33</li><li>Schiffers, R.; Kruppa, S.; Moser, S.: Für jeden Schuss der richtige Umschaltpunkt. Kunststoffe 104 (2014) 11, S. 58–63</li><li>Pillwein, G.; Willnauer, P.; Steinbichler, G.: Opening up a Broader Range of Applications for Recyclates. Kunststoffe international 110 (2020) 4, S. 26–30</li><li>Schiffers, R.; Morik, K.; Struchtrup, A. S. et al.: Anomaly detection in injection molding process data based on unsupervised learning. Zeitschrift Kunststofftechnik/Journal of Plastics Technology 2018 (2018) 1, pp. 301–347</li><li>Schmitz, M.; Hopmann, C.; Röbig, M.: Beyond Human Capabilities – Model-Based Process Setup by Fully Interconnected Production in Injection Molding. Kunststoffe international 109 (2019) 7, pp. 89–893</li><li>Bergmann, F.-J.; Dörner, R.; Drach, M.; et al.: Industrie 4.0 in Spritzgießunternehmen. VDI Verein Deutscher Ingenieure e. V.; VDI-Statusreport Februar 2019</li><li>Ryntz, R. A.: The influence of compositional variations in compounded thermoplastic olefins (TPOs) on the physical and mechanical attributes of injection molded plaques. Journal of Vinyl & Additive Technology (1997) 3, pp. 295–300</li></ol>

Read the German version of the  article in our magazine Kunststoffe or at  <a href="https://www.kunststoffe.de/a/fachartikel/hocheffiziente-adaptive-faserverbund-rot-325976" target="_blank">www.kunststoffe.de</a>