Mannheim, January 1st, 2022

Digital prototyping as an engineering support service

The most important points at a glance

  • Integrating simulation in engineering processes shortens the time to operational readiness
  • Unlike physical prototyping, digital prototyping is sustainable because it conserves resources
  • Simulations can be tightly integrated in product lifecycle management (PLM)

Digitalized engineering facilitates virtual prototyping, which in turn provides its own benefits. The use of simulations in the product development process makes it possible to significantly reduce the number of prototypes required and thus also reduces the materials and energy needed. This is especially true when development and simulation are performed to a large extent in parallel.

When design engineers check the viability of their solution using an initial version, the analysis data provides them with information on the best possible dimensions and combination of materials for the system elements at an early stage. This approach makes it possible to create solutions that achieve operational readiness faster and almost always function smoothly right from the start.

Following commissioning, simulations based on the operational data of networked systems indicate potential for improvement. They help reduce wastage and component wear, and they extend the lifecycle of the system.

To avoid having to create new computational models for flow, thermal and other weak-point analyses for system variants, the models can be stored centrally, together with the analysis results, in systems for simulation process and simulation data management (SPDM). This makes it possible for users at any location to reuse and easily adapt the models for new simulations. As a result, engineering benefits not only from the growing wealth of experience but also from simulation standards and partially automated, digital processes between the engineering and simulation teams.

Attributing simulation results to design versions in PLM

Companies that manage their product data in a PLM system have an opportunity to improve the traceability of their digital prototypes. To do this, the simulation methods, including the parameters of the individual load cases and the associated analysis results, can be clearly attributed to the CAD model used via an integration.

This not only allows the quality of the respective development status to be traced and documented, but also means that the simulation teams benefit from the tried and tested functions that PLM offers, such as version control, version comparison, workflows, and release management.

  • An export process controlled via the bidirectional system integration transfers the test data to the SPDM system and sends the simulation reports from the SPDM to the storage location in the PLM system defined for this purpose.
  • If additional PLM information is relevant for the simulation performed using the CAE model, the integration generates a corresponding data package that also includes the metadata. The PLM product structure remains intact.
  • If PLM-specific information needed for an analysis is missing, the user from the simulation team searches for it directly in the PLM system and imports it into their analysis system.
  • An integrated messenger system simplifies communication between the teams.

Digital prototyping for multi-disciplinary system optimization

The trend in product development is clearly moving towards the cross-domain engineering of functional models. What this means for the simulation process is that multiple authoring tools are involved and their data has to be synchronized so that valid alternatives for further engineering can be determined.

The challenges involved are demanding:

  • The creation of simulation models for system functions that bundle data from requirements management, mechanical, electrical and electronic engineering, and software in a way that the simulation program can interpret.
  • Output of the analysis results in a way that allows them to be used in the different domains to optimize the overall system as desired.

Today, the function algorithms for control-related applications, such as the nozzle of a 3D printer or an engine management unit, are determines to a very great extent by the software. This means that system validation increasingly focuses on the effectiveness of the algorithms and the (automatic) adaptation of the corresponding code.

This means that in many cases it is easier to represent system variants using different configurations of a system designed to meet maximum requirements, i.e. a single nozzle with different programs for multiple products or a single engine with different performance levels configured for multiple models. An approach such as this reduces the need for prototyping to a prototype of the maximum system.

When it comes to optimizing entire (embedded) systems, there are systems whose algorithms examine models for the defined quality criteria and display the results as graphical representations. Some are available as open-source solutions. As for the systems for optimizing mechanical components, the systems for prototyping complete system functions also allow standard models to be created as the simulation elements of a computational toolkit. They can all be integrated in the product engineering process (PEP) using intelligent connectors.

The University of Stuttgart is offering a seminar on creating simulation software for the first time in the winter semester 2021/2022.