Visual Analytics methods are used to measure and compare production processes in Industry 4.0. This is important, for example, when analyzing changes or errors in production output. We offer solutions for all industries that enable comparisons between different production periods and states by quickly visualizing large data sets.
The Visual Analytics team develops customized solutions for every need and use case, enabling the rapid implementation of customized, interactive analytics applications for millions of data sets, which opens up a completely new dialogue with data for all industries. The focus is on multivariate analyses, time series as well as regression analyses.
All the data that accumulates in Industry 4.0 is important information that, when correctly prepared, not only improves the explanation of existing processes, but above all enables forecasts for the future. Visual Analytics offers a range of presentation options here, above all predictive modelling, which can predict future results based on real data. Here it is essential to always focus on evaluating the quality of the data because models can only be as good as the data on which they are based.
In the En2VA research project, an existing analysis platform was expanded into a communication tool, for example for joint workshops between data scientists and experts, and optimized for processing extremely large volumes of data with millions of entries. This makes En2VA the ideal Visual Analytics solution for Industry 4.0, for example in the area of simulation-based product development, which enables both the testing of data quality and the creation of forecast models and their evaluation to be carried out quickly and easily.
In our applied research project TOHIVA we developed novel Visual Analytics methods relevant for Industry 4.0 for the more efficient visualization of large, high-dimensional data for customized forecast models to support decision-making processes.
The digital twin and its fields of application are among the most important topics in Industry 4.0, where companies are facing major challenges due to the digital transformation. As a digital copy of a real object or as a model of something that will be created in the future, the digital twin makes it possible to run through production processes from the initial idea to production and to simulate a wide variety of scenarios in a very short time. The preparation and accompaniment of real production processes by digital twins not only saves costs and resources but also allows a better understanding of the data and thus, for example, more accurate predictions in a production plant.
Immersive technologies offer solutions for dealing with the large amounts of data that routinely accumulate in Industry 4.0. With the help of Extended Reality (XR), even Big Data can be made accessible to people: for example, by processing these huge amounts of data in Virtual Reality. This makes the data literally tangible - the user can stick his head into the 3D data. The combination of Extended Reality and Immersive Analytics makes it possible to break down the last barriers between humans and data.
Extended Reality (XR) is used in Industry 4.0 for planning, control and production. Virtual Reality training is particularly suitable for training courses, allowing employees to immerse themselves in highly realistic environments and events, thus providing them with a profound learning experience. VR training is particularly suitable for complex or even risky training courses.
A VR fire protection training simulates different fire scenarios and several fire extinguishing methods. The worst-case can be played through without any danger: Users learn very clearly what the fatal consequences of wrong behaviour are or what unsuccessful extinguishing looks like when the simulated fire spreads in real-time and devours the VR living room. Fire fighting in VR is clearly superior to its real counterpart in terms of safety, practicability and selection of training scenarios.
Based on real-world use cases from the field of intelligent logistics systems, the REINFORCE project investigates how reinforcement learning can solve complex control problems.
Im Projekt REINFORCE wird anhand von realen Anwendungsfällen aus der Praxis intelligenter Logistiksysteme untersucht, wie durch Reinforcement Learning komplexe Steuerungsprobleme gelöst werden können.
Training AI algorithms requires a great amount of data. However, raw data often contains sensitive information. Homomorphic encryption offers a solution for secure machine learning - with protected sensitive data.
This project aims at accelerating and automating image-based decision making with an application focus on medicine, recycling and quality assurance processes in manufacturing.
The primary goal of project INGRESS is to accelerate and improve the process of data scientists working with Industry 4.0 and Internet of Things (IoT) data, by enabling a closer integration of visual analysis into the existing workflows.
The RAILING project deals with the research and development of interactive, scalable and trust-building visualization and analysis tools for the exploration of time-dependent and complex data.
The goal of the project MARAMT is to develop a software framework to significantly reduce the effort required to work with existing and future complex cyber-physical systems.
An augmented reality solution for optimizing process development in laboratories and monitoring ongoing experiments supports pharmaceutical research.
New technologies and security policy developments bring a paradigm shift for the Austrian armed forces. The use of information and communication measures entails both challenges and opportunities: for combat, security and rescue manoeuvres.
The goal of the applied research project En2VA (“Visual Analytics for Energy and Engineering Applications”) is to increase the efficiency and the quality of advanced analytics for high-dimensional data from manufacturing, engineering, and the energy sector.
Fire training for non-professionals is expensive, complicated and dangerous. The solution: a simulation in a virtual environment.
This project is dedicated to training and workflows in Mixed Reality.
In this project we develop new methods from the field of visual analysis and machine learning to automate the quality control and quality assurance of glass articles.
In this project tools and methods for handling, administration, manipulation and evaluation of several different data sources for measurements and lighting design are developed.
With Augmented Reality, simulation results of car engine noises become visible.
Novel visual analysis technologies for high-dimensional data in automotive engineering, industrial manufacturing and the energy sector
A more efficient way to create operating manuals from existing databases for product life cycle management using Augmented Reality.
Strategic Research in Scalable, Semantic Rendering.
Investigation of techniques enabling a seamless analysis of data from multi-run simulations on multiple degrees of detail.
Research and development of novel interactive visualization methods for visualization and understanding of complex systems.
Visual Analytics for Modeling and Simulation: Improvement of simulation setup and design scenarios with tools and methods of Visual Analytics.
Improving and combining multiple sensors to increase the accuracy and reliability of modern surveying equipment.
Decision support systems and 3D viewing technologies for tunnel construction.
Next generation workflows for interactive knowledge generation from images and simulations.