ProDiNA
Innovative, digital and resource-saving solution for prototype development and testing in the context of pump prototyping on the digital twin.
Product tests not only consume a lot of resources, but often also represent a capacity bottleneck and thus limit optimization of the products in terms of efficiency and sustainability. In the ProDiNA project, this bottleneck is resolved by a digital, scalable approach to prototype testing, thereby achieving numerous resource savings during product development. The approach is to be tested specifically with the digital twin of pump prototypes.
A digital twin is a digital copy of a prototype that contains all relevant data needed for testing. This digital twin can then be used to run simulations, reducing or even replacing physical testing. The digital approach also opens up new possibilities for prototype testing, such as the ability to test multiple variations of a prototype at the same time, thereby increasing flexibility. In addition, the digital sustainability pass enables a comprehensive view of all relevant product data and information, which in turn facilitates the optimization and recyclability of the end product and creates the basis for transparent communication regarding the sustainability of a product (material properties, recyclability, resource consumption).
Our role in the project
As part of the research project, the August-Wilhelm Scheer Institute is dedicated to research work in the area of developing a digital twin and the digital sustainability passport for the pump prototypes, as well as the development of AI algorithms to predict the service life of the pumps and the early detection of possible weak points or defects.
Digital twin
Goal: Conception and development of a digital twin for pump prototypes and development of a digital sustainability passport
Solution:
- Requirements analysis taking into account pump and material characteristics as well as the requirements for simulation and AI-based forecasts
- Conception and development of a basic framework for the digital twin, which is constantly growing as the project progresses and the newly acquired data is ingested
- Creation of the initial digital twin and ongoing validation and optimization
- Creation of a digital sustainability passport based on the data collected in the digital twin, which represents the data basis for a digital product passport of the finished pump
Machine Learning
Goal: Development of AI models to predict key performance indicators and to detect defects in physical testing at an early stage
Solution:
- Analysis of existing data from previous physical prototype testing
- Comparison and linking with the parameters obtained from the simulation
- Development of AI models, using methods from machine learning and deep learning, to predict the pump service life and to identify possible weak points using a digital prototype based on data from physical testing and simulation
- Creation of models for early defect detection during physical prototype testing using anomaly detection based on various sensor data
- Testing and validation of the developed models through digital and physical testing
Project management
Goal: The overall coordination of the project and ensuring the scientific and technical progress in connection with the financial aspects of the project. Administrative project tasks and partner communication as well as cooperation with other projects. Dissemination of the results and public relations.
Solution:
- Use of agile project management techniques and tools
- Infrastructure for collaboration, document management
- scientific and non-scientific publications of the results
The initial situation
Overview of the need for action
Traditional, physical prototype testing is currently associated with a large use of resources. Defects that occur during the test phase and thus events that make it unnecessary to continue testing are currently not usually recognized immediately after they occur. In addition, the clear identification of the cause of the defect, which is necessary in order to further develop the prototype, represents a challenge. In future it will also be necessary to be able to provide precise information and proof of the sustainability of products and their components. The database required for this has not yet been created.
Traditional prototype testing is resource-intensive
Defects and their causes have so far been difficult to identify
Your contact person
ProDiNA
Intelligent prototype testing on the digital twin for sustainability optimization of drive systems
Laura Bies
laura.bies@aws-institut.de
+49 162 2934 499
Your contact person
ProDiNA
Intelligent prototype testing on the digital twin for sustainability optimization of drive systems
Laura Bies
laura.bies@aws-institut.de
+49 162 2934 499
Our solution approach in focus
In the project ProDiNA emissions and materials can be saved by means of the desired digitalization of pump prototype testing. In addition, the test cycle of the physical prototypes is shortened and optimized. This promotes broader testing and thus also a far-reaching optimization of the end product. For this purpose, a precise digital twin of the physical prototype is created, which provides all the necessary data for testing using simulation and machine learning. For this purpose, the digital twin is enriched with data such as geometry data, material characteristics and sensor measurements.
Based on the digital twin and the individual test components, a software platform is developed that accompanies the entire digital prototype construction and test. The resulting flexibility and increased efficiency of prototype testing enable further tests to optimize the end product. In particular, the Sustainability of the product are in focus. Specifically, energy consumption, durability and failure rate as well as the material composition are to be considered and optimized. If, for example, a defect is identified during this digital test, this finding can shorten the duration of the physical test and thus save resources. In addition, the digital twin enables the test to be terminated in good time. In addition, the type and cause of the defect can also be precisely identified, which is crucial information for the continuous optimization of the prototypes.
Based on the data obtained, a Digital sustainability pass about the respective material properties in terms of recyclability and second-life material use. This digital sustainability pass represents a partial aspect of the future mandatory digital product pass, which is considered a core element of the circular economy. The ProDiNA project is thus realizing solidly tested cycle-optimized products, including transparent proof of the recyclability of the products through the digital sustainability pass.
Funding notice
The ProDiNA project is funded by the Federal Ministry for Economic Affairs and Energy.
Funding code: 01MN23016A
Running time: 01.05.2023-30.04.2026
