DFA

Digital fingerprint.

Creating a basis for an intelligent value chain

Your contact person

Silvio Facciotto

Institute of Aircraft Design (IFB), University of Stuttgart

Digital fingerprint

Creating a basis for an intelligent value chain

Intelligent data collection, processing and transfer along the entire value chain - from the idea, through design, production and in-service to end-of-life - for the intelligent component and the versatile, autonomous factory of the day after tomorrow!

Vision

The context of Industry 4.0 requires the implementation of a digital fingerprint for each individual component. This fingerprint serves as a holistic representation of the entire product life cycle, whereby product development, manufacturing, assembly and monitoring are realized through automated, self-organized and intelligent processes.

Objective

The intention is the coherent recording and intelligent networking of data throughout the entire development process: from the initial concept, through design and process data, to data recorded in-situ using integrated sensors. The application of the digital fingerprint is intended to ensure significant reductions in development times and costs as well as production waste.

Course of the project

The project was divided into five sub-projects:

  1. Data and semantics: Semantic characterization of the digital fingerprint.
  2. Smart engineering: Elaboration of various simulation approaches, from component optimization to the predictive representation of potential component damage under real load scenarios.
  3. Smart component: Integration of adequate sensor technology in the component to record data from both the manufacturing process and the application phase.
  4. Smart production: Implementation of an efficient interface to production and assembly.
  5. In-service and evaluation: Evaluation of the digital fingerprint based on real in-service data.

Results in detail

SP1 - Data and semantics

The Teamcenter tool was used as the PLM system for central data management, which provides a comprehensive data model for the digital fingerprint of all components (DFA). The architecture shown in the illustration supports the collection of data from both production processes and ongoing operations. In addition, a web-based portal was designed that allows users to initiate measurements remotely. The data stored in the system is not only used for quality monitoring, but also flows into various simulation models.

 

SP2 - Smart engineering

Simulation models have been developed that depict both production processes and operating loads. These models allow the collected data to be directly integrated into the product development process. Virtual methods are used to continuously optimize processes and designs for subsequent product generations. In particular, the simulation of component loads in real operation, based on data from road tests, is an essential factor in the early detection and prediction of component failure. This paves the way for predictive maintenance, which can be individually adapted to customer requirements such as type of use, climatic conditions and terrain.

 

TP3 - Smart component

During the production phase, MEMS sensors were integrated into demonstration components. These sensors specialize in recording accelerations and temperatures, enabling continuous monitoring of both the production process and the condition of the component.

 

SP4 - Smart production

All production processes are continuously monitored to maximize production efficiency. This allows potential sources of error to be identified in advance, which minimizes the unnecessary consumption of resources and time. The data collected during production is recorded using sensor technologies developed in SP3. Technologies from SP1 are used for monitoring and data acquisition. The collected data is used in combination with simulation models from SP2 to continuously optimize and personalize CAM programs.

 

TP5 - In service and evaluation

Driving tests can be carried out by integrating demonstrators into vehicles. These serve to realistically forecast the service life of individual components. The knowledge gained from this is directly reflected in the design processes and closes the loop between the real product life cycle and its digital counterpart.

Starting points and follow-up projects

Future developments and synergies could manifest themselves in projects such as CATENA-X, ARENA2036-X and EcoFrame.

"ARENA2036 is a beacon of the University of Stuttgart in the field of knowledge and technology transfer. In collaboration with our partners from science and industry and not least the start-ups, we have succeeded in building a unique innovation platform. Personally, I am particularly fascinated by the agility and adaptability of ARENA2036, which is reflected in the fact that I am constantly discovering new projects in the hall space."

Prof. Dr.-Ing. Peter Middendorf

Head of the Institute of Aircraft Design

University of Stuttgart

"In the production of the future, data will play a decisive role and the integrated use of development, production and operating data will be the key factors for trustworthy value creation and utilization systems. ARENA2036 is already a special environment for DXC.technology. As a leading, independent integration service provider, we can contribute to the networking of these worlds and help shape the data standards of tomorrow. The university environment is of fundamental importance for the training of our young talent."

Gero Adrian & Franziska Gross

Director Advisory R&D Industry4.0 at DXC Technology & Business Innovation and Startup Relations

DXC Technology