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Machine Learning for Automotive Material Parameter Calibration

30 Chapters
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01
Automotive material parameter calibration
role in CAE, crashworthiness, NVH, and durability; challenges of manual calibration and motivation for machine learning.
Automotivematerial
02
Material models for automotive applications
elastoplasticity, viscoelasticity, hyperelasticity, damage, and failure criteria; parameters requiring calibration and their physical meaning.
Materialautomotive
03
Experimental characterization for calibration
tensile, compression, shear, bulge, and split Hopkinson pressure bar tests; extracting stress-strain data and identifying calibration targets.
Experimentalcharacterization
04
Calibration workflow
from test data to simulation validation; inverse problem formulation, objective functions, and error metrics for automotive material models.
Calibrationworkflow
05
Design of experiments and sampling for calibration
Latin hypercube, Sobol sequences, and adaptive sampling; generating training data for machine learning surrogates.
experimentssampling
06
Simulation setup for parameter calibration
finite element model preparation, boundary conditions, mesh convergence, and automation of simulation runs for data generation.
Simulationparameter
07
Introduction to machine learning for calibration
supervised learning, regression, and surrogate modeling; mapping between material parameters and simulation responses.
calibrationsupervised
08
Gaussian process regression for material parameter calibration
kernel selection, hyperparameter optimization, and uncertainty quantification in automotive applications.
Gaussianregression
09
Neural network surrogates for calibration
multilayer perceptrons, convolutional neural networks for full-field data, and training strategies for simulation-based problems.
surrogatescalibration
10
Random forest and gradient boosting for calibration
feature importance, handling high-dimensional parameters, and comparison with other surrogates.
gradientboosting
11
Dimensionality reduction and feature extraction
principal component analysis, autoencoders, and their use in calibrating material parameters from full-field measurements.
Dimensionalityreduction
12
Inverse problem solving with machine learning
direct inverse mapping, iterative optimization with surrogates, and hybrid approaches for automotive material calibration.
iterativeoptimization
13
Bayesian optimization for calibration
acquisition functions, batch optimization, and efficient exploration of parameter space for expensive simulations.
Bayesianoptimization
14
Multi-objective calibration
Pareto front identification, trade-offs between different test responses, and decision making for automotive material models.
Multi-objectivecalibration
15
Uncertainty quantification in calibrated parameters
aleatoric and epistemic uncertainties, propagation to simulation predictions, and robust calibration.
Uncertaintyquantification
16
Calibration of elastoplastic models
yield surface, hardening parameters, and kinematic hardening; case study on automotive steel and aluminum alloys.
Calibrationelastoplastic
17
Calibration of viscoelastic and hyperelastic models
polymer and rubber components; time-temperature superposition and large deformation behavior.
Calibrationviscoelastic
18
Calibration of damage and failure models
GISSMO, Johnson-Cook, and cohesive zone parameters; application to crash simulation of automotive structures.
CalibrationGISSMO
19
Calibration of composite material models
orthotropic elasticity, Hashin criteria, and progressive damage; challenges for carbon fiber reinforced polymers in automotive body structures.
Calibrationcomposite
20
Calibration of battery and thermal management materials
thermal conductivity, specific heat, and electrochemical parameters; machine learning for multiphysics calibration.
Calibrationmanagement
21
Calibration of NVH materials
viscoelastic damping, acoustic absorption, and frequency-dependent properties; surrogate modeling for trim and body panels.
CalibrationNVH
22
Calibration of manufacturing process parameters
stamping, injection molding, and welding; linking process simulation to material behavior for automotive parts.
Calibrationmanufacturing
23
Data-driven calibration from full-field measurements
digital image correlation, infrared thermography, and machine learning for spatial parameter identification.
Data-drivencalibration
24
Transfer learning and domain adaptation for calibration
leveraging data from similar materials or loading conditions to reduce experimental effort.
Transferadaptation
25
Active learning for calibration
selecting informative experiments and simulations to minimize calibration uncertainty and cost.
calibrationselecting
26
Integration of machine learning calibration into CAE workflows
automation, software interfaces, and deployment of surrogates in commercial solvers.
Integrationcalibration
27
Validation and verification of calibrated models
cross-validation, blind validation, and metrics for automotive crash and durability simulations.
Validationverification
28
Case study
machine learning calibration of a high-strength steel for crash simulation; from tensile tests to full vehicle model validation.
calibrationhigh-strength
29
Case study
calibration of a rubber bushing for NVH using Gaussian process and Bayesian optimization; experimental design and surrogate accuracy.
calibrationNVH
30
Future trends and challenges
physics-informed machine learning, digital twins, real-time calibration, and integration with autonomous vehicle development.
challengesphysics-informed