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Related Experiment Video

Updated: May 12, 2025

Surrogate Model Development for Digital Experiments in Welding
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Graph-based process models as basis for efficient data-driven surrogates - expediting the material development

Johannes Gerritzen1, Andreas Hornig1,2,3, Maik Gude1

  • 1Institute of Lightweight Engineering and Polymer Technology (ILK), TUD Dresden University of Technology, Holbeinstr. 3, Dresden, 01307, Germany.

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|May 8, 2025
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Summary
This summary is machine-generated.

This study introduces a new method to represent material development processes (MDPs) using graph-based models and "flowthings." This approach accelerates material development by enabling data-driven modeling and optimization.

Keywords:
Data-driven decision makingProcess developmentSurrogate modeling

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Area of Science:

  • Materials Science and Engineering
  • Computational Materials Science
  • Process Modeling and Optimization

Background:

  • Increasing complexity and cost pressure necessitate more efficient material development processes.
  • Traditional experimental approaches in material development are time-consuming and expensive, posing a significant bottleneck.
  • Data-driven models are gaining traction but require structured process representations for effective training.

Purpose of the Study:

  • To propose a novel formalism for efficiently representing material development processes (MDPs).
  • To demonstrate the application of this formalism in the development of high modulus steel (HMS).
  • To accelerate MDPs through data-driven modeling and inverse optimization.

Main Methods:

  • Developed a formalism combining graph-based process models and the 'flowthings' concept.
  • Derived a directed acyclic graph (DAG) representation of the MDP from acquired data.
  • Trained various black-box surrogate models using a derived database and selected best models based on RMSE.

Main Results:

  • Successfully derived a structured DAG representation for the high modulus steel (HMS) development process.
  • Trained and evaluated multiple surrogate models, identifying best-performing models via RMSE.
  • Utilized selected models for inverse optimization to maximize specific modulus under design constraints.

Conclusions:

  • The proposed formalism enables efficient data representation for material development processes.
  • Data-driven surrogate modeling accelerates decision-making and optimization in material development.
  • This approach has significant potential to reduce development cycles and costs in materials engineering.