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Multidisciplinary optimization of automotive mega-castings merging classical structural optimization with

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  • 1ALTAIR Engineering GmbH, Calwer Str. 7, 71034, Böblingen, Germany.

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This study introduces a new optimization pipeline for designing large automotive castings (mega-castings). The method enhances lightweight design by integrating topology optimization, response-surface-based optimization, and machine learning for structural analysis.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Science

Background:

  • Large high pressure die castings (HPDC), or mega-castings, offer lightweighting potential and manufacturing cost savings for automotive body-in-white (BIW) structures.
  • BIW structures must meet stringent requirements for vehicle dynamics, noise, vibration, harshness (NVH), comfort, and passive safety.
  • Integrating mega-casting designs presents challenges in castability, material quality, and achieving lightweight goals under complex load conditions.

Purpose of the Study:

  • To develop a generative multidisciplinary optimization pipeline for the structural design of automotive mega-casting parts.
  • To exploit the full lightweight potential of mega-castings by addressing multiple design constraints simultaneously.
  • To improve the efficiency and effectiveness of BIW structural design compared to traditional workflows.

Main Methods:

  • Combines topology optimization for load-path derivation with response-surface-based (RSM) optimization for thickness and rib design.
  • Enhances RSM optimization using machine learning (ML) for clustering and classification of simulation results.
  • Incorporates casting manufacturing constraints and over a hundred linearized loads for NVH and passive safety requirements.

Main Results:

  • The proposed pipeline effectively integrates multidisciplinary requirements, including crashworthiness and castability, into the design process.
  • Machine learning-based classification of simulation field results improves the robustness of optimization, avoiding issues with purely scalar targets.
  • The approach demonstrates superiority in achieving weight-optimal designs and reducing turnaround time compared to conventional BIW design workflows.

Conclusions:

  • The generative multidisciplinary optimization pipeline offers a powerful approach for designing lightweight automotive mega-casting structures.
  • The integration of ML-enhanced RSM optimization addresses the challenge of simultaneously considering complex performance indicators and simulation constraints.
  • This methodology enables more efficient and effective realization of lightweight automotive components while meeting critical performance and manufacturing requirements.