VIP20
本课程属于：013-Six Sigma for CAE LS-DYNA and Occupant Restraint Engineering English Technical Courses · 共 20 门相关课程，组合学习更完整。
查看所属VIP全部课程 →
← 返回所属目录 咨询 / 购买

Python-Driven Six Sigma Analytics and LS-DYNA Batch Studies

30 Chapters
View Course Samples Online
01
Course overview
integrating Six Sigma DMAIC with Python analytics and LS-DYNA batch simulation for engineering quality improvement.
integratingDMAIC
02
Six Sigma fundamentals for simulation-driven design
CTQ identification, Y=f(X) thinking, and the role of CAE in DMAIC.
simulation-drivenCTQ
03
Python environment setup for Six Sigma and LS-DYNA automation
pandas, NumPy, SciPy, statsmodels, matplotlib, and subprocess control.
environmentLS-DYNA
04
LS-DYNA input deck structure and parameterization
keyword files, include files, and identifying design variables for batch studies.
LS-DYNAstructure
05
Automating LS-DYNA runs with Python
subprocess, job scheduling, directory management, and parallel execution strategies.
AutomatingLS-DYNA
06
Extracting and parsing LS-DYNA results with Python
d3plot, binout, and ASCII output files; using lasso-python and pyDyna for data extraction.
ExtractingLS-DYNA
07
Measurement systems analysis for simulation
verification, validation, and uncertainty quantification of CAE models as a measurement system.
Measurementanalysis
08
Process capability and performance metrics for simulation outputs
Cp, Cpk, PPM, and handling non-normal response data.
capabilityperformance
09
Exploratory data analysis of batch simulation results
distributions, outliers, and visualization with pandas and seaborn.
Exploratoryanalysis
10
Design of experiments (DOE) for LS-DYNA
factorial, fractional factorial, and optimal designs using pyDOE2 and scipy.stats.
experimentsDOE
11
Generating and managing DOE matrices in Python
randomization, blocking, and coupling with LS-DYNA input decks.
Generatingmanaging
12
Running a full factorial DOE batch study
Python orchestration, result collection, and building a tidy results DataFrame.
factorialDOE
13
ANOVA and regression analysis of simulation DOE data
main effects, interactions, and model adequacy checking with statsmodels.
ANOVAregression
14
Response surface methodology (RSM) with Python
central composite designs, model fitting, and contour plots for optimization.
Responsemethodology
15
Sensitivity analysis and variance decomposition
Sobol indices, Morris screening, and Saltelli sampling using SALib.
Sensitivityanalysis
16
Surrogate modeling for LS-DYNA
kriging, polynomial regression, and neural network metamodels with scikit-learn.
Surrogatemodeling
17
Model validation and cross-validation of surrogate models
R2, RMSE, and prediction interval assessment.
validationcross-validation
18
Multi-objective optimization using Python
Pareto front generation with pymoo and integration with LS-DYNA simulations.
Multi-objectiveoptimization
19
Robust design and tolerance analysis
Taguchi methods, signal-to-noise ratios, and Monte Carlo simulation with Python.
toleranceanalysis
20
Case study
crashworthiness optimization—batch LS-DYNA runs, DOE, RSM, and robust design for automotive frontal impact.
crashworthinessoptimization
21
Case study
sheet metal forming—springback compensation using Python-driven DOE and LS-DYNA batch simulations.
springbackcompensation
22
Case study
drop test reliability—probabilistic analysis and Six Sigma capability assessment for electronic packaging.
reliabilityprobabilistic
23
Automating report generation
Python templates (Jinja2, ReportLab) for Six Sigma project documentation and CAE result summaries.
Automatinggeneration
24
Building a Python dashboard for Six Sigma analytics
interactive visualization with Plotly Dash or Streamlit for simulation results.
Buildingdashboard
25
Workflow orchestration and reproducibility
version control, configuration management, and pipeline tools (Snakemake, Airflow) for LS-DYNA studies.
Workfloworchestration
26
High-performance computing integration
Python submission to HPC clusters, job arrays, and result aggregation for large batch studies.
High-performancecomputing
27
Quality function deployment (QFD) and CTQ flowdown for simulation
linking customer requirements to LS-DYNA design variables via Python.
functiondeployment
28
Measurement system analysis for simulation data
gauge R&R adapted to CAE, and Python implementation for variance component analysis.
Measurementanalysis
29
Control phase for simulation-driven design
statistical process control charts for monitoring simulation pipeline stability.
simulation-drivenstatistical
30
Capstone project
end-to-end Six Sigma DMAIC with Python and LS-DYNA—from problem definition to optimized, validated design and control plan.
Capstoneend-to-end