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.

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

Spritzgießen

Kunststoffverarbeitung: Verfahren

Künstliche Intelligenz für die Qualitätssicherung

Industrie 4.0

Industrie 4.0 für die Qualitätssicherung

Digitale Transformation

Wirtschaft

Innovationen und neue Technologie

Vernetzung / Cloudlösung für die Kunststoffverarbeitung

Industrie 4.0 für die Kunststoffverarbeitung

IoT-Plattformen für die Qualitätssicherung

Fernwartung / Ferndiagnose

Instandhaltung

Produkt- / anbieterspezifische Automatisierung

Automatisierungstechnik

Automatisierungszellen

Energieeffizienz

Fertigungsautomation

Datenanalyse

IoT-Plattformen für die Kunststoffverarbeitung

“Intelligent Automation Brings Systems Closer to their Optimum”

The cluster of excellence “Internet of Production” at the RWTH Aachen University conducts research on structures to realize a “Google of Production”. The Plastics Innovation Center 4.0 serves as demonstration platform to present real-world solutions to the industry already today.

IKV Offers an Application-Oriented Demonstration Platform for Excellent Research

The PIC 4.0 as Part of the Internet  of Production

A year to forget is nearing its completion. To brighten up this dreary fall season without a major event like Fakuma, injection molding machine builders have concocted various strategies.

Injection Molding Machine Builders Are Discovering New Ways to Reach Their Customers

Spritzgießmaschinen, allgemein

Kunststoffverarbeitung: Maschinen und Komponenten

Spritzgießmaschinen

Biokunststoffe

Kunststoff Werkstoffe

Nachhaltigkeit

Umweltmanagement

Schneidmühlen

Kunststoff-Aufbereitung

Silikonkautschuk

Kautschuk / Elastomere

What Can You Do at Fall Fairtime,  When There Aren’t Any?

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

A Process Monitoring Platform Monitors Several Hundred Parameters Simultaneously

Steuerungen (Hardware und Software)

Prozessvisualisierung

Condition Monitoring / Prozessüberwachung

Spritzgießsimulation

Software

Simulation

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).

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

Messdatenerfassung und -verarbeitung

Steuern, Regeln

Prozessoptimierung

Prozessmanagement

Machine Learning

Kunststoff-Rezyklat

Focus on the User Benefit

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.

Innovative Injection Molding in Times of Pandemic

Schaumspritzgießen

Mehrkomponenten-Spritzgießen

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

Spritzgießwerkzeuge

Werkzeuge für die Kunststoffverarbeitung

Spritzgieß-Sonderverfahren

Schnecken und Plastifiziersysteme

Machine Learning / Künstliche Intelligenz für die spanende Fertigung

Industrie 4.0 für die spanende Fertigung

Kunststoff-Recycling

Lasermarkieren

Oberflächentechnik

Laserbearbeitungen

Inline- / Online-Messsysteme

Qualitätssicherung

Qualitätssicherung Kunststoff

Kreislaufwirtschaft

There Is More beyond the Horizon

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.

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.

Successfully Managing Digital Transformation with Automation and Artificial Intelligence

Kameras, intelligente

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.

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.

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

Extrusion

Doriat, Clemens, Dr.

Ressourcenmanagement

Produktion

Compoundieren

Moving with the Times

M-Hub is the first mobile App that helps entrepreneurs to promote their business and find all players in the plastic industry with the tap of a finger.

The Plastic Industry in Your Hand

Corona is also driving digitization in the used machinery trading. The global second-hand market for metal and plastics processing machinery is undergoing structural change.

Kunststoffverarbeitungsmaschinen

Gebrauchtmaschinenhandel

Dienstleistungen

Dienstleistungen in der Metallbearbeitung

Gebrauchtmaschinen

Sondermaschinen

Unternehmen

kunststoffe.de

Online Trading with Used Machines Booms in Corona Times

With the help of the coupling station, fast material changes become normal practice, material sources such as dryers, roller containers or external silos can be used by several consumers, and RFID monitoring ensures a faultless production process.

Kunststoffverarbeitung allgemein

Kunststoffverarbeitung

Materialhandling

Betriebstechnik für Kunststoffverarbeitung (Peripherie)

Material Distribution Monitoring

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.

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

The Circular Economy is not only the theme topic at the K 2019. It is also the “green” thread running through the fair exhibit by Engel, the provider of injection molding machines and integrated system solutions. Industry 4.0 is turning out to be a driving force for recycling. This is a most timely insight, since the branch is in for several economically cool years.

Engel Supports Strong Involvement by the Plastics Industry in a Circular Economy

Verbundwerkstoffe / Werkstoffverbunde

Co-Injektion (Barriere-/Sandwich-Spritzgießen)

Organobleche (gewebeverstärkte Thermoplast-Halbzeuge)

Halbzeuge und Zwischenprodukte

Kunststoff-Halbzeuge

A Second Life for Plastics

Standardization Guarantees High Data Quality for Future Applications

A consistent flow of data and information is essential for digital solutions and meaningful analytics. The introduction of potential solutions is therefore often associated with high costs for data preparation. Part 3 of the report on the Plastics Innovation Center 4.0 (PIC 4.0) at RWTH Aachen University describes information flow planning, which ensures a consistent database for future process optimizations as well as increased transparency.

A consistent flow of data and information is essential for digital solutions and meaningful analytics. The introduction of potential solutions is therefore often associated with high costs for data preparation.

Due to standardized interfaces and increasingly uniform communication protocols, data acquisition is still an important and necessary field of development but no longer a major challenge. Modern machines and peripherals as well as software systems are able to provide a large amount of data that has to be analyzed and archived. The disillusionment is all the greater when the collected data hardly offers any added value. The reasons for this are often found in a lack of consistency and missing data points, which prevent a semantic linking of data to information as well as an automated analysis of data [1, 2]. It is therefore not uncommon that data preparation takes up to 80 % of a data scientist’s working time before the actual analysis of the data begins.

Necessity of a Consistent  Information Architecture

For data to become information, it must be semantically linked to a use case or analysis focus and prepared in a way that can be interpreted by a decision-maker. If individual data packages are missing or have been labelled differently by another person, this makes interpretation more difficult. Examples of this are production data such as processing temperature or pressure that are not assigned to an equally continuously measured quality feature, or the sequence in which the cylinder temperature zones are labelled starting from the nozzle or material feed. In particular, the individual perspective of different operators and handwritten machine logs, which are subsequently digitized at great effort, increase the potential for interpreting individual data points in a database incorrectly or not at all.

Although the PIC 4.0 only represents a research infrastructure for the development of intelligent production systems for plastics processing, the challenges of consistent data and information structures in science and industry are similar. In order to illustrate a consistent and expandable labelling of the gathered data as well as a consistent interaction of all linked systems and objects in a Smart Factory, the Institute for Plastic Processing (IKV) at the RWTH Aachen University implements an information architecture in the PIC 4.0 after the model of the Reference Architecture Model Industry 4.0 (RAMI 4.0) <a href="http://[3]">[3]</a>.

Cross-Domain Information Flow throughout the Value Chain

The information flow can and must be considered and modeled on different levels of abstraction (Fig. 1). When linking the individual departments of a smart manufacturing system, a distinction can be made between different types of data, which are exchanged via direct M2M communication along the value chain or via information exchange with a central server.

Master data refers to identification and general data for the production or test order as well as other data that are unchangeable over time or are considered as unchangeable, e. g. mold and target product dimensions or the characterization of the machine and peripheral equipment used. Material data are mandatory at many points along the value chain, such as for mold design in product and process development or for targeted process control in the technology center.

Simulation data is mainly generated in development and can then be used for data-driven process setup in the technology center [4, 5]. Process data of manufacturing processes and material handling, in turn, are gathered as digital shadows in order to implement a direct data-driven process optimization or to feed these data back into the development department for a targeted comparison of the simulated with the actual product quality.

On the next level, the generic data groups from the graphic (Fig. 1) can be more precisely considered along trial processing (analogous to order processing). Depending on the manufacturing process or separated process steps, quality and productivity relevant data are generated along the material flow during value creation of a product, which must be linked across all processes (Title figure).

According to the RAMI 4.0 hierarchy axis, the information flow is no longer considered on the company level, but on the level of the work centers e. g. across different manufacturing cells in production. Due to the more detailed representation of relevant information and data flows across several manufacturing levels, semantic correlations to a specific application or to quality-relevant conclusions from process data can be intuitively mapped.

Both the basic categorization and the specific assignment to the respective production steps and data sources support the creation of meaningful databases and process-specific synchronization across several manufacturing levels.

Modeling the Data Flow with the Help of Activity Diagrams

The detailed modeling of the data flows of individual process steps in activity diagrams specifies the information flow of the respective manufacturing processes. An example from blown film extrusion shows extracts of the individual process steps before the subsequent calibration of the blown film on the cooling section, independent of the machinery equipment (Fig. 2).

The respective setting values and measurable process parameters are directly assigned to the corresponding process steps and allow conclusions to be drawn about the relevant process section and the data source.

The activity diagrams of the PIC 4.0 processes are used to assign relevant setting and monitoring parameters in a process-specific manner and to consistently label the respective process parameters. Based on a uniform understanding of the variables in the process and the correlation between controllable setting parameters and relevant actual values, an information system can be set up that can be intuitively understood and expanded.

Through the object-oriented programming and implementation of an information architecture based on a reference architecture like the RAMI 4.0, data acquisition systems can be automatically configured in the future. Thus, it is ensured that gathered data can be interpreted for the employment of intelligent solutions in plastics processing continuously and with low effort.

RWTH Aachen Inst. f. Kunststoffverarbeitung IKV 

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

https://www.ikv-aachen.de

Ausgabenartikel

Fig. 2. Extract from activity diagrams of blown film extrusion with assigned input and output data (Station-Layer)

Exemplary information flow across several steps of the value creation process in injection molding(Work-Center-Layer)

Fig. 1. General perspective of information flow planning in the PIC 4.0 with exemplary links between separated domains of a Smart Factory (Enterprise-Layer)

Name - Hauptartikel

The Reference Architecture Model Industry 4.0 (RAMI 4.0) was defined in 2016 in DIN SPEC 91345 and describes a reference architecture in the form of a cubic layer model for the representation of technical systems and objects in a smart production environment. Via its three axes (architecture axis; progression axis; hierarchy axis) it describes the interaction and the mandatory information exchange of interconnected objects across the value chain <a href="http://[3]">[3]</a>.

Univ.-Prof. Dr.-Ing. Christian Hopmann 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.

Pascal Bibow, <a href="http://M.Sc">M.Sc</a>. 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).

In the following article, the authors report on the importance of the PIC 4.0 as a demonstration platform for industry-related developments in the Internet of Production. Presumably, it will be published in issue 03/2021. The previous parts have been published in issues 5/20 and 7/20.

[1] García, S.; Ramírez-Gallego, S.; Luengo, J.; Benítez, J. M.; Herrer<a href="http://www.kunststoffe-international.com/2020-10">www.kunststoffe-international.com/2020-10</a>d prospects. Big Data Analytics 1 (2016) 1

[2] Amin, R.; Arefin, T.: The Empirical Study on the Factors Affecting Data Warehousing Success. International Journal of Latest Trends in Computing 1 (2010) 2, S. 138–142

[3] DIN Deutsches Institut für Normung e. V.: Reference Architecture Model Industrie 4.0 (RAMI 4.0). Beuth Verlag GmbH, Berlin 2016

[4] Schmitz, M.; Hopmann, C.; Röbig, M.; Pelzer, L.; Topmöller, B.; Wurzbacher, S.: Jenseits menschlicher Fähigkeiten. Modellgestützte Prozesseinrichtung durch vollvernetzte Produktion im Spritzgießen. Kunststoffe 109 (2019) 9, S. 142–145

[5] Hopmann, C.; Bibow, P.; Kosthorst, T.; Lockner, Y.: Process-setup in injection moulding by Human-Machine-Interfaces and AI. In: Hopmann, C. (Hrsg.): 30th International Colloquium Plastics Technology. Aachen 2020, S. 157–196

Read the German version of the  article in our magazine Kunststoffe or at  <a href="http://www.kunststoffe.de">www.kunststoffe.de</a>