Based on interviews in industry, this article describes a common understanding of the most important use cases. The results capture the current direction in the plastics industry.

16:9

Part 1 of the Series: Use Cases Based on Strategic Priorities in Plastics Processing

Strategie und Organisation

Wirtschaft

Spritzgießen

Kunststoffverarbeitung: Verfahren

Industrie 4.0 für die Kunststoffverarbeitung

Industrie 4.0

Compoundieren

Kunststoff-Aufbereitung

Implementation of Industry 4.0 in Injection Molding and Compounding

How do you bring 65 injection molding machines and various assembly lines – most of the manufacturing cells in the cleanroom – as well as, soon, a large number of ancillary equipment under one digital roof? A field report.

The Arburg host computer system ALS is the link between the shop floor and ERP system and connects order management with process and quality monitoring.

The Arburg Host Computer System as a Basis of Higher Productivity and Product Quality

Steuerungen (Hardware und Software)

Automatisierungstechnik

Industrie 4.0 für die Qualitätssicherung

Doriat, Clemens, Dr.

Condition Monitoring / Prozessüberwachung

Instandhaltung

Kennzahlen, Q-Leitstand, Q-Monitor

Qualitätssicherung

Rückverfolgbarkeit, Traceability

Qualitätsmanagement

Energieeffizienz

Montagelinien

Montagetechnik

Montage

Medizintechnik

Reinraumsysteme / -technik

Medizinprodukte

Case Files for Patient Safety

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.

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

Messdatenerfassung und -verarbeitung

Software

Machine Learning

Datenanalyse

Datenbanken

Big Data für die Qualitätssicherung

Qualitätssicherung Kunststoff

IoT-Plattformen für die Kunststoffverarbeitung

Complete Data without Ifs and Buts

The iQ product family from Engel will gain a new member at K 2022. iQ hold control automatically determines the optimum holding pressure time for the particular mold with no input from the operator.

 The optimum holding pressure time is often lower than that established at the beginning. In these cases, the cycle time is shortened.

iQ hold control Determines the Optimum Holding Pressure Time

Smart Factory

Spritzgießmaschinen

Kunststoffverarbeitung: Maschinen und Komponenten

Energiedatenmanagement

Holding out for Higher Quality

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

Mehrkomponenten-Spritzgießen

Spritzgießwerkzeuge

Werkzeuge für die Kunststoffverarbeitung

Nachhaltigkeit

Umweltmanagement

Spritzgießsimulation

Simulation

Kreislaufwirtschaft

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

Digitale Transformation

Innovationen und neue Technologie

From the Insulin Pen Cap to the Automotive Door Module

At K 2022, Engel, with its partners Alpla Group, Brink and IPB Printing, is presenting a quantum leap for the packaging industry. Thin-wall containers can for the first time be produced from PET directly by injection molding in only one process step.

Fig. 1. The thin-walled containers are injection molded from rPET.

Schmelzefilter

Polyethylenterephthalat

Kunststoff Werkstoffe

Thermoplaste

Kunststoff-Rezyklat

Processing rPET by Thin-Wall  Injection Molding

Netstal presents new solutions for high-performance injection molding in Düsseldorf.

aXos control unit with “Smart Operation:” the guided machine operation enables users to achieve additional productivity increases.

Fast, Precise and Networked

Under the motto “It‘s all Wittmann”, Wittmann Battenfeld is presenting its machines and systems in the new Wittmann design for the first time.

Automation cell with SmartPlus 90 for manufacturing a spirit level.

Schaumspritzgießen

Spritzprägen

Biokunststoffe

Naturmaterialien

Verstärkungsstoffe

Green DC Current for the Injection Molding Machine

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

Precise Process Control with Injectix

This year, the world’s leading trade show K has succeeded in presenting practical solutions for greater resource efficiency on a broad front. A first foray through the trade show grounds.

K 2022 gave numerous practical examples showing visitors how recyclates can be reliably processed with digital aids.

State-of-the-Art Injection Molding Solutions for Greater Resource Efficiency.

State-of-the-Art Injection Molding Solutions at K 2022

Solar / PV

Energieerzeugung

Erneuerbare Energie

Temperiertechnik

Handling, Handhabungstechnik

Elastomer-Spritzgießen

Oberflächentechnik

Digitalisierung

Silikonkautschuk

Kautschuk / Elastomere

It Can Be Done!

Phase-unifying cavity pressure control reduces process fluctuations, as shown by the example of post-consumer recyclate processing.

The PCR storage box was very popular with visitors.

Phase-Unifying Cavity Pressure Control for PCR Processing

Laserbeschriftung

Laserbearbeitungen

Mess-, Steuer- und Regelgeräte

Präzisionswaagen

Mechanische Messtechnik

Precise Process Control with InjectiX

The iQ motion control assistance system from Engel optimizes the acceleration phases of mold movements and thus shortens cycle times.

The pressure to work efficiently is huge, especially in the packaging industry. The production of thin-wall containers clearly shows the potential of  iQ motion control to shorten cycle times.

iQ motion control Shortens Cycle Times in High-Performance Injection Molding

Elektrische Antriebe für Spritzgießmaschinen

Antriebe

Elektrische Antriebe

How about a Little Bit Faster?

Whether it is the cycle time, energy consumption or process stability: the levers for greater efficiency and quality in the injection molding process are many and varied. With the new performance.boost Engel offer its customers an expertise as a service for process analysis and optimization.

With its expert knowledge, the company identifies process optimization potential and provides user support during their implementation. The results include shorter cycle times, superior part quality, greater process stability, lower energy consumption or a longer machine service life

Kunststoffverarbeitung

Dienstleistungen

Lohnfertigung

Instandhaltungssoftware

Optimize Processes Efficiently with Engel performance.boost

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

Werkzeug- und Formenbau

Werkzeug-, Formen- und Modellbau

Sensoren verschiedener Messverfahren

Fertigungsautomation

Sensorik / Aktorik

Fernwartung / Ferndiagnose

Dienstleistungsqualität

Instandhaltungsmanagement (TPM)

Vorbeugende Instandhaltung für die Qualitätssicherung

Werkzeugbau

Machine Learning für die Qualitätssicherung

Symbiosis of Toolmaking and  Serial Production

On the Way to the Smart Injection Molding Factory

Part 2 of the Series: Integrated Data Acquisition for the Injection Molding Production

Everyone is talking about digitalization and Industry 4.0. In practice, however, implementation is often difficult. The data-driven operation of an injection molding production is often still a dream. Only if the exact actual condition of the machines and peripheral equipment is known, work on optimizing the processes can be done. The Eastern Switzerland University of Applied Sciences describes the challenges that have to be overcome and shows possible solutions based on a self-operated manufacturing cell.

There are two main challenges in implementing Industry 4.0 today [1].

On the one hand, there is often a lack of awareness in practice, both for possible applications and for the necessity of a concrete step-by-step implementation. We are talking about the actual target orientation or also use case definition for the smart factory. This concretization must be oriented to the following questions:

<ul><li>How can I increase my value creation by using data?</li><li>How can I use my data? What is the goal?</li><li>What data do I need to implement a specific use case?</li></ul>

The awareness for possible applications can be created by the use case framework presented in <a ext="tango:{f7174548-8ad9-4017-b06b-55b8e552a559}">the last article of this series (Kunststoffe international 7/21)</a>. This framework was systematized and developed on the basis of realized use cases in practice [2].

On the other hand, learning from data requires a complete database. Some questions arise here as well:

<ul><li>Which signals do I need? Which signals are available at all?</li><li>What quality of data do I need? Is the data available in this required quality?</li><li>How do I get the data out of my machine?</li><li>How do I synchronize data from different machines and devices?</li></ul>

The database is often a major hurdle, as the data is simply not available or not in sufficient quality. In the field of plastics processing, for example, there are no suitable, industry-wide standardized specifications and protocols that can be integrated with a variety of different data sources and database systems without any problems. This lack of standardization has resulted in each machine providing data differently today. Fortunately, many standards and specifications are currently in development (e. g. OPC UA), so interoperability will improve in the future.

Investigations showed that a step-by-step approach to implementing Industry 4.0 in plastics processing is target-oriented. We start with the definition of the use case. Based on this, an initial model for prediction can be developed with a focus on this use case. The validated model can then be integrated into the IT landscape, which is then rolled out in the factory or the entire production network. This is also reflected in the developed model for the implementation of Industry 4.0 in the factory (Fig. 1).

Many expert organizations are currently also dealing with this topic and specifically with the standardization of data. For example, there are also recommendations from the VDI group on which signals should be recorded in the field of injection molding [3].

Newer machines offer increasingly better data availability, i. e. they also enable more and more high-frequency recording of the individual signals and their storage as time series curves. This enables in-depth process analyses, which are essential depending on the use case. For the cyclic export of process parameters, the Euromap 63 and Euromap 77 (OPC UA) interfaces also play an important role here.

At the Institute of Materials Technology and Plastics Processing (IWK), it was possible to set up a data acquisition system to which five different injection molding machines from various manufacturers and various peripheral devices have now been integrated. The recorded data can be visualized and evaluated directly.

A number of challenges had to be overcome in order to set up this type of data acquisition for machine learning. It became clear that device-specific solutions often had to be implemented so that the data could be exported and recorded in the desired quality. This is a hurdle for smaller manufacturing companies in particular, as such interdisciplinary know-how is often not available.

Another difficulty is the synchronization of data from different machines and devices (e. g. injection molding machine, temperature control units, ambient conditions). Each device usually works with its own time stamp. This is a common problem, especially when working with time series of different devices. This can be avoided by setting up a live system in which all data is recorded synchronously. The time stamp is assigned by the data acquisition system. However, this requires that all machines and devices provide the data live. This means that curve data, which for example is made available via the OPC UA interface at the end of the cycle as time series histories, is not suitable for this purpose. A direct query of strongly time dependent signals (e. g. injection pressure or screw position) via OPC UA in high frequency is not suitable due to the limited publishing interval of the interface (usually max. 200 to 500 ms). However, it is possible to query pre-calculated parameters (such as maximum or average values) at the end of each cycle, and these data can also be combined with curve data. Nevertheless, aggregation already results in a loss of information. Depending on what is to be learned from the data, this can of course be critical. Older machines, which do not support such interfaces and do not store raw data at all due to limited storage capacities, present an additional difficulty.

In addition to synchronization, the allocation of data from different pre or post-processes is also an issue. On the one hand, data acquisition differs between continuous processes, e. g. material drying, in which continuous time series are recorded, and discontinuous processes such as injection molding and, for example, welding as a follow-up process, in which the data are recorded for each cycle and at a defined sampling rate. On the other hand, the data from the pre-process steps must then also be able to be clearly assigned to the later data of the part. For injection molding, this means which material batch was processed and how long was the material used, to manufacture the molded part, pre-dried? This is not a simple question, which is why it is currently still being worked on. For follow-up processes, this is much easier, since the part and the associated data already exist and only need to be expanded.

Fully Automated and Self-Learning Manufacturing Cell

One implementation of this connected factory is a manufacturing cell for the production of a floorball (Title figure and Fig. 2). The ball halves are manufactured on an injection molding machine, laser marked, completely measured in three dimensions and then stored in an interim storage system sorted by color. A collaborative robot is used for automated handling.

For customer orders, this robot takes the ball halves in the desired color from the storage system and transfers them to the welding machine, where the halves are welded into a ball fully automatically. This is a fully connected and automated production cell, the process data of all production steps as well as the quality characteristics are stored in the cloud and can be clearly assigned to each ball. On the one hand, this ensures complete traceability and, on the other, the entire production process can be optimized by using artificial intelligence and advancing towards zero-defect production. This manufacturing cell serves as a training object for students of the OST –Eastern Switzerland University of Applied Sciences and shows companies the possibilities of digitalization.

Use of Data Acquisition for  Research Projects

The data acquisition system also serves as the basis for the use of artificial intelligence in a wide range of projects in the field of injection molding. A central topic is the early detection of process anomalies on the basis of process data. When an anomaly occurs, the machine operator should also be given a recommendation for a suitable countermeasure. This topic is being investigated in a three-year publicly funded research project with five industrial partners.

The process anomalies can be reliably detected and first appropriate countermeasures can be suggested. With the use of a support vector machine and an autoencoder an anomaly score (Fig. 3, top left) can be calculated based on various features. This indicates how stable the current process is running. When this score increases, a recommendation for action is suggested to the operator (Fig. 3, bottom right), which can be used to correct the process.

Other use cases include the prediction of the quality characteristics of the manufactured parts on the basis of process data and predictive maintenance or detection of wear on tool or machine components.

[1] Hopmann, C. & Schmitz, M. (2021). Plastics industry 4.0: Potentials and applications in plastics technology. Hanser Publishers; Hanser Publications.

[2] Budde, Lukas, Hänggi, Roman and Friedli, Thomas. „Digitalisierung à la carte: Individuelle Auswahl von Use Cases nach strategischen Prioritäten“ Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 113, no. 12, 2018, pp. 850–854. <a href="https://doi.org/10.3139/104.112016" target="_blank">https://doi.org/10.3139/104.112016</a>

[3] VDI-Gesellschaft Materials Engineering (2019). VDI-Statusreport Industrie 4.0 in Spritzgiessunternehmen.

OST Ostschweizer Fachhochschule Inst. f. Werkstofftechnik u. Kunststoffverarbeitung (IWK)

https://www.iwk.hsr.ch

Ausgabenartikel

Fully automated manufacturing cell for the production of a floorball at IWK’s technical laboratory in Rapperswil, Switzerland

Fig. 1. Model for the implementation of machine learning in the factory

Fig. 2. Production flow of fully automated manufacturing cell

Fig. 3. GUI (Graphical User Interface) for anomaly detection and countermeasure proposal 

Ostschweizer FH - Hauptartikel

Curdin Wick, M.Sc., is head of the injection molding department at the Institute of Materials Technology and Plastics Processing (IWK) at Eastern Switzerland University of Applied Sciences (OST).

Prof. Dr.-Ing. Frank Ehrig heads the Institute for Materials Technology and Plastics Processing (IWK) at OST.

Prof. Daniel Schwendemann is head of the compounding and extrusion department at the Institute of Materials Engineering and Plastics Processing (IWK) at OST.

Prof. Dr. Roman Hänggi is a partner at the Institute for Product Design, Development and Construction (IPEK) and Professor for Production Management at OST.

Adrian Rüedy, M.Sc., is project manager at the Institute for Product Design, Development and Construction (IPEK) at OST.

The last article in this series takes a look at the challenges involved in implementing specific use cases and learning from data with the help of artificial intelligence in the field of compounding production technology.