The IoT in practice trail demonstrates the performance of databased apps on the way to autonomous manufacturing. It focuses on customized analysis and diagnosis applications for engineers and machine operators. With the help of these digital-assistants vast amounts of raw data is permanently processed throughout the whole process chain. This data helps to simplify the users working processes by using automatically computed recommendations for action and also helps to reduce resource consumptions, machine downtime, rejects or critical stress situations.

The core of the trail is the direct interchange with expert users of real process chains in lightweight constructions and plastics processing, which serve to make the benefits of IoT tangible for the participants.

All demonstrating sites are equipped with the IoT apps based on the IoT-Dashboard Detact™. They provide an overall data consolidation and analysis of process-, quality- and material-data retrieved from distributed and heterogeneous data sources.

Visitors will witness and understand how a global view of a certain process chain is created due to IoT and which methods make process chains mathematically describable. They will also learn how mathematics – encapsulated in the IoT apps – gets usable for engineers to quantify interactions within the process chain in order to optimize the processes. The participants can experience each showcase at selected facilities, gain valuable inspirations concerning the usage of IoT in their own factory and engage in cooperative exchange and discussion on the digitizing strategy of manufacturing processes.


Smart Systems Hub GmbH
Postplatz 1
01067 Dresden
+49 351-48 18 88 97

Target Groups:

The showcases are aimed at production- and process managers as well as officers for smart factory of the plastics processing industry (injection moulding, extrusion, thermoforming process, composites), metal processing industry (aluminum-, zinc and magnesium die casting) and the fields of application of material characterization and generative manufacturing.

The target groups in particular:

CEO/Factory manager with the following mission:

  • orientation on a digitizing strategy to secure competitiveness
  • maximizing the processes’ technological security to manufacture high demanding products
  • reducing manufacturing costs (reject costs and personnel expenses)
  • improving monitoring and efficiency (investments in the facilities‘ durability)
  • striving for customer satisfaction – IoT to serve as an argument for attracting customers

Expert user with the following mission:

  • Willing to develop an extensive process understanding of cause-effect correlations in manufacturing processes
  • Reducing stress and increasing flexibility in process management
  • Reducing stress caused by the commissioning of new products
  • Interest in new technologies (autonomous production, preventive maintenance)

IT department with the following mission:

  • interest in technological solutions for the digitalization of production technology
  • interest in technical architectures of IoT and in possibilities to direct connect them to heterogeneous data sources like facilities or up to excel sheets

Multipliers with the following mission:

  • interest in experiencing real life examples of IoT in production environments
  • searching for possibilities to get in direct dialogue with expert users and software producers of an IoT platform

Partners from research & industry:

TU Dresden, Institut für Leichtbau- und KunststofftechnikFiber composite showcase FuPro — injection moulding to fibre-thermoplastic composites4 hrs.
DGH Group, DGH Heidenau GmbH & Co. KGDPressure die-casting showcase4 hrs.
Leichtbau-Zentrum Sachsen GmbHMaterial characterization showcase3 hrs.
FEP Fahrzeugelektrik Pirna GmbH & Co. KGInjection moulding showcase/td>3 hrs.
JKL Kunststoff Lackierung GmbHLacquering showcase3 hrs.
Symate GmbHCoordinator, Vendor of the IoT platform Detact™6 hrs.

Value proposition:

Normally, process managers have a particular interest in process data in the event of problems or when optimum setup parameters are required to commission a new product. And so the whole process chain is monitored and more and more data is measured and recorded. But during the processing time the potential of the permanently recorded production data is lost. Furthermore, due to the data’s distribution, amount and heterogeneity and originating from different sources (e.g. machine control units, sensors, databases or up to handwritten protocols) there is a high effort or it’s even impossible to connect data in order to detect parameter correlations throughout the entire process. However, the IoT in practice trail reveals what it means to never lose track of your data but learn from it in the running process. The aim of it is to accomplish a recording, processing and evaluation of data streams assisted by a high level of automation – or to put it otherwise: IoT for the production industry.

In part 1 of the trails (Teaser) the participant gets an understanding of the methods and the architecture of the IoT platform. With real-life examples, complex process chain models are developed and methods of automatic analyzing services plus connection techniques for connecting external data sources are introduced. It’s also illustrated in which scenarios IoT platforms can be typically applied in production and which ambitions are aimed to achieve by the user group. In addition, the requirements are specified regarding the data basis and the necessary expert skills in analysis methods.
On the other hand the potential for statistically and technologically data services will be examined based on sample data of the participants. Especially the estimation, if existing technological problems can be processed with the available and potentially recordable process data, is in focus. This includes the following steps:

  • collection and optionally preselection of potentially production processes (selection of facility systems; which steps of the manufacturing process chain should be examined)
  • description of the actual technological situation and specification of the technological type of problem
  • predefinition of the performance measurement in the end of the project: economic criteria, requests, estimation of the feasibility, rating of the potential portability to other processes
  • investigating the technological framework of the data collection (machine connection, manual gathering, data formats, etc.)
  • estimating expectable project results, e.g. increased controllability (and its probabilities) as well as optimization potential and its valuation
  • surveying and evaluating project risks
Figure 1: Detact Apps for anamoly detection in process chains, © Paulsberg OHG

As a result, the participant is able to evaluate the capability of a data driven approach for its individual issues. In part 2 of the trails the participant obtains information about how IoT applications work and how they can be customized or completely newly developed to meet the very own needs. Symate will provide knowledge regarding the setup of the main settings of an IoT platform: mapping of the users and devices; configuration of parameters as well as for the fundamental handling on the basis of Detact™: data recording and processing functions, process chain modelling functions, parameter selection and prioritizing, plus process data analysis and the basics of complex analysis such as regression, sensitivity analysis, correlation analysis.

Based on that, the participant will face real IoT scenarios in the production environment. The different stations of the trail are structured according to various stages in the production technology as follows:

  • Smart Ramp-Up (Demonstrators ILK, LZS)
  • Smart Diagnostics (Demonstrators DGH, JKL)
  • Smart Control (Demonstrator FEP)

In sequentially ordered showcases the different use cases, significant for technologists, process engineers, production managers or maintainer, will be demonstrated. The practicability of IoT apps for the daily work of the prospective user group is open to discussion with the respective practice partner. To make IoT projects calculable to the trails participants, the demonstrated benefits can be transferred to the specifics of the visitor’s peculiar process chains. Such as:

  • reducing rejects due to gained insights via IoT apps,
  • improvement of availability, quality and performance (e.g.: increasing OEE by >10%),
  • discharge of routine tasks in root cause analysis by up to 90%,
  • increased customer satisfaction due to individual component notes for customers,
  • minimized down times of the process chain due to recognizing potential disruptive factors before occurrence.


Teaser Trail (Part 1) or Intensive Trail (Part 1 and Part 2)



The Institute for Lightweight and Plastics Processing (TU Dresden, Institut für Leichtbau- und Kunststofftechnik – ILK) is developing new manufacturing processes for resource efficiency lightweight structures in regional, nationwide and international research projects in cooperation with its partners. Their showcase demonstrates what opportunities are provided by IoT to establish closer cooperation among all partners.

The automated cross-process data collection and transfer with IoT offers outright new methods of analysis due to the relativeness between all parameters in the process chain. As a result, correlations in the process chain are detectable, which wasn’t possible using previous process chain analysis methods.

Figure 2: Detact Apps for the support of start-up processes, © Paulsberg OHG

The showcase contains an IoT demonstrator consisting of the following components:

Process chain:Arburg injection moulding machine including production complex
IoT platform & applications:

  • Detact IoT-Dashboard incl. process chain modeler, configuration management, etc.
  • Detact Connect Drivers for the automated direct connection of the data source ARBURG injection moulding machine including production complex and numerical preprocessing of raw data as well as parameter identification (e.g. cycle-related analysis’s).
  • Manual Data Collection App for test-related acquisition of process-, quality- and context-information by facility operators, collected half-automated via mobile displays.
  • Set-Up Report App for report generations of samplings including trial series, technology data, detected correlations and recorded process windows.

Manufacturing process chains in pressure die-casting cover more than thousand technological relevant parameters. The setting up and monitoring of process chains is highly complex and prone to error. In this showcase the participant gets an insight on how IoT can support engineers and facility operators in diagnostics of process disruptions only through connectivity and intelligent analytics.
This contains:

process chain: Bühler Carat Pressure Die-Casting Machine C250 incl. periphery with vacuum unit, dosing furnace and integrated software systems e.g. data base GUSS.
IoT platform & applications:

  • Detact IoT-Dashboard
  • Detact Connect driver for automated direct connection of more than 30 data sources. All data formats of machine controllers, tool sensors, material and quality checks or of the corporates IT department are collected.
  • Conflict Finder App for identification and visualization of vulnerabilities in the process chain. The app classifies process events (e.g. faulty parts or machine down times), evaluates them and permits a drill-down functionality to display the particular process condition.
  • Root Cause Analysis App to interactive root cause analyze disruptions (e.g. down times, rejects or discrepancies in cycle times) based on an overall parameter inspection of the process chain to visualize differences between targets/actual figures or respectively interpret similarities from mismatches.
  • Process History App on behalf of the visualization of the process history. With this app it is possible to evaluate process conditions in consideration of the overall parameters like mechanical incidents including quality metrics etc.
  • Part Tracking App to track the condition of the parts on the basis of the corresponding technology data. Via the tracking app the manufacturing process of all parts is comprehensible across the entire process chain.

The material characterization showcase demonstrates how IoT can take on important tasks in the fields of raw data processing and numerical parameter determination. The performance of transferring the results from the manufacturing processes is represented by the following components:

process chain: mini-press machine to manufacture carbon test pieces
IoT platform & applications:

  • Detact IoT-Dashboard including functionality for explorative data analysis, process chain modelling and services for data preprocessing and creation of analyzability, configuration management, etc.
  • Detact Connect driver for automated direct connection of an press machine and a tensile test system
  • Data Exploration App for explorative data analysis to derive parameter correlations due to free selection, filtering and comparison of parameters collected from all connected sources.
Figure 3: Detact Apps for quality prediction and reduction of measuring effort, © Paulsberg OHG

FEP Vehicle Electrics Pirna GmbH & Co. KG (FEP Fahrzeugelektrik Pirna GmbH & Co. KG) is producing plastic plug connections for the automotive industry. Influencing factors to secure an economical production are the optimizing of cycle times, improvements in process and quality capabilities as well as accelerating ramp-up processes after conversion or down times. The participant gets an impression about the usability of an automated processing of incoming data and the consequently use of design of experiment methods (DoE).
The showcase contains of:

process chain: Arburg injection moulding facility 570C incl. linear robot multilift
IoT platform & applications:

  • Detact IoT-Dashboard including functionality for explorative data analysis, process chain modelling and services for data preprocessing and creation of analyzability, configuration management, etc.
  • Detact Connectdriver for automated direct connections of 4 data sources: Arburg injection moulding facility (via ProSeS connection), the automated connection to Babtec-CAQ with internal measuring records from testing facilities and the automated processing external measurement results (external testing lab).
  • Design of Experimentsfor model constructions of parameter correlations
  • Quality Predictionfor validating the actual process condition in correspondence to the part’s quality metrics based on a continuously computed error probability.
  • Recommended Action App for the creation of setup recommendations in the event of process and quality discrepancy. The app uses technological and statistical models for the optimization of process windows (DoE).

The business activities of JKL Plastics Paint GmbH (JKL Kunststoff Lackierung GmbH) concentrate on the lacquering of thermoplastic composites and assemblies primary for the automotive industry as well as for more different industry products from plastic. JKLs strategic aim is to gain a certain understanding about fluctuations in processes and their impacts and roots by applying IoT based on the incoming data streams. Besides, the demonstrator will explain which requirements are need to fulfill to support process managers with their troubleshooting.
The showcase consists of the following components:

process chain: a fully automated lacquering line equipped with robotics technology for lacquering thermoplastic composites; assemblies primary in the automotive industry.
IoT platform & applications:

  • Detact IoT-Dashboard for data preprocessing to establish analyzability, configuration management etc.
  • Detact Connectvdriver for automated direct connection of 5 data sources: lacquering line via Beckhoff TwinCat ADS, various excel sheets, specimen lists, status reports and robotic programs.
  • Root Cause Analysis Appto interactive root cause analyze disruptions (e.g. down times, rejects or discrepancies in cycle times) based on an overall parameter inspection of the process chain to visualize differences between targets/actual figures or respectively interpret similarities from mismatches.
  • Anomaly Detection Appas the name suggests to detect anomalies in process events (e.g. process disruptions)

Opportunities for Projects:

Implementing pilot projects with Detact™ basic installation

With its customizability Detact™ paves the way to a step by step introduction into the subject of the digitalization of manufacturing process chains. Typical basic installations start with up to 3 data sources and can be expanded to productive systems with additional data sources or with increased data capacity. Expensive and long-term implementation projects with large project teams are prevented, instead the lean and flexible approach of Detact™ establishes a usage of data amounts which is incoming from the production process anyway as well as an immediate supply of usable results.
Finally, the participants are also introduced to funding opportunities provisioned by SAB and BMWi suitable for small and medium-sized enterprises.

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