best practices and use cases for data-driven engineering...2020/12/02 · data understanding:...
TRANSCRIPT
-
Best practices and use cases for
data-driven engineering
Dr. Stefan Suwelack [email protected]
-
Present use
cases by role ?Test
SimulationDesign
PLM
2© Renumics GmbH
-
Use horizontal
categories Surrogate models
Data exploration
& understanding
Generative
design
Process
automation
3© Renumics GmbH
-
5
Data understanding: Assessment of DoEs
Example: Crash simulation
Improve robustness
Understand optimality criteria
Understand critical correlations
© Renumics GmbH
© Constantin Diez, Lasso Engineering
Classic method:
Principal component analysis
(PCA)
-
Deep autoencoders
& representation
learning
Advantages:
1. Geometry independence
2. Generalized task-specific
similarity measuresembedding
6© Renumics GmbH
-
Data understanding: Assessment of test data
Example: Acoustic measurement data
Find outliers and reference points
Identify root causes
Find errors in test setup
Classic method:
Spectral features + shallow ML
DL methods:
CNN autoencoder, LSTMs
7© Renumics GmbH
-
Process automation: Geometry processing
Recognition and measurement of
complex geometric features
Time saving: >70%
Assessment of collisions (packaging /
DMU)
Time savings: > 50%
8© Renumics GmbH
-
Process automation: Simulation assessment
Assessment of simulation results for
NVH
Mode classification >95% accuracy
Simulation monitoring
Reduction of simulation time, significant error
reduction
9© Renumics GmbH
-
Surrogate modeling
Example: Digital twin of turbine blade
Speed-up
Efficient optimization loops
Digital Twin / IOT applications
© Dynardo GmbH
Classic methods (examples):
Response surface models (parametric),
Proper orthogonal decomposition
(fields)
10© Renumics GmbH
-
Deep learning
based surrogate
modeling
Advantages:
1. Generalizes over different
simulation settings
2. Geometry can be
parameter – free
Big trend: Physics-informed
neural networks© NVIDIA 2020
11© Renumics GmbH
-
Generative
Modeling
Classic methods:
1. Parametric models
2. Topology optimization +
reverse engineering +
manufacturing rules
12© Renumics GmbH
-
Generative
Modeling with DL
Advantages:
Automatic selection of implicit
parameters
Big challenge: Dataset
selection and extrapolation
© Autodesk 2017
13© Renumics GmbH
-
Best practices for
successful ML
projects
1. Define business case
2. Understand available data
3. Build data strategy and tooling
4. Define user interaction with ML-tool
5. Prototype algorithms
6. Prototype process integration
7. Build trust with users
8. Roll-out and operate
14© Renumics GmbH
-
Building and
curating datasets
Historic Data
+ already available - messy
+ distribution well
captured
- siloed
Synthetic Data
+ standardized setups
and formats
-expensive to obtain
+ lower bureaucratic
obstacles
-Difficult
parameterization
- biased datasets
16© Renumics GmbH
-
Dataset curation: Similarity map examples I
Simulationsrun by intern
High modelerror: Noconvergence
17© Renumics GmbH
-
Dataset curation: Similarity map examples III
Difficultexamples
Bad annotations?
19© Renumics GmbH
-
Similarity
maps
Collaborative ML-model development:
Discuss relevant data properties with
domain experts
Annotate quickly and reliably
Discuss model performance
Build user trust
Analysis of complex engineering data:
DoE workflows: Simulation result
assessment
Test data: Find points of interest
20© Renumics GmbH
-
Powerful filters
Interactive similarity map
Highly configurabledetailed view
-
Renumics
Spotlight
Fast data exploration & ML-model design
Curate datasets for ML training
Understand large amounts of simulation
data quickly
Analyze test data and optimize test setups
Modular integration into existing tools
Easily integrates into Python-based
workflows
Can be enhanced with Renumics
Backstage: Customized notebook for data
science beginners
Uses open standards to integrate into an
open ecosystem of ML-tools
22© Renumics GmbH
-
We help great engineers to
understand their data
Dr. Stefan Suwelack [email protected]
Daniel Klitzke [email protected]
Steffen Slavetinsky [email protected]