AI/ML-based Prediction of Crash Parameters using ODYSSEE CAE
A Hexagon MI / MSC Software case study showing how Satven used ODYSSEE CAE (LUNAR) machine learning to predict energy absorption and force for a crush can across different material and thickness combinations — reducing prediction time from multiple hours to a few seconds and removing the need for simulation software for initial predictions.
Headlines at a glance
The headline numbers and findings as they appear in the report — designed for fast scanning by leadership teams.
Crush-can crash parameter prediction with ODYSSEE CAE (LUNAR)
ODYSSEE CAE reduced the time for predicting effects of different materials and t
After ML training, energy-absorption and force predictions occur without running
LUNAR was trained on energy absorption and force data for crush cans across mult
Hexagon MI / MSC Software — Satven case study
What the report actually says
Major discoveries and conclusions from AI/ML-based Prediction of Crash Parameters using ODYSSEE CAE.
Multi-Hour Predictions in Seconds
ODYSSEE CAE reduced the time for predicting effects of different materials and thicknesses on crush-can crash parameters from multiple hours to a few seconds.
No Simulation Software for Initial Predictions
After ML training, energy-absorption and force predictions occur without running full CAE simulation.
Material x Thickness Training
LUNAR was trained on energy absorption and force data for crush cans across multiple material grades (e.g., W6) and various thicknesses (0.8 mm, 1.0 mm).
Strategic Capability for Automotive Bureaus
Satven developed competency in AI/ML-based solutions to strengthen competitive advantage on automotive engineering work.
Executive Summary
A concise overview of the report's scope, methodology and conclusions.
Satven adopted ODYSSEE CAE (LUNAR) to forecast energy absorption for crush cans across specific material and thickness combinations, employing machine learning that compresses prediction time from multiple hours to seconds.
Once trained on existing CAE simulation data, the ML model generates initial predictions for new material/thickness combinations without full CAE resimulation, significantly accelerating product cycle time and enhancing Satven's competitive positioning in crash and crashworthiness analysis for global OEMs.
Topics & sections covered
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Section preview
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Satven company background
Crush can energy absorption — challenge
ODYSSEE CAE (LUNAR) AI/ML training and prediction
Result comparison: CAE vs LUNAR
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What You'll Learn
- Crash parameter prediction compressed from hours to seconds
- ML eliminates the need for full CAE simulation for initial predictions
- Training methodology across material grades and thickness combinations
- Validation approach: comparing ML predictions against conventional CAE results
- Accelerated product cycle time for automotive crashworthiness analysis
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AI/ML-based Prediction of Crash Parameters using ODYSSEE CAE
A Hexagon MI / MSC Software case study showing how Satven used ODYSSEE CAE (LUNAR) machine learning to predict energy absorption and force for a crush can across different material and thickness combinations — reducing prediction time from multiple hours to a few seconds and removing the need for simulation software for initial predictions.
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