Spot Welds, NVH, Clutch Prediction with Dataraft
Engineering Analytics Revolution: From Spot Weld Optimization to NVH Customer Insights and Clutch Failure Prediction with Dataraft
Engineering organizations generate massive datasets across the product lifecycle - high-fidelity simulations, BOM rollups, road test telemetry, dyno runs, and unstructured customer feedback narrations. Yet these remain siloed in folders, PLM systems, or Excel, forcing engineers to rely on tribal knowledge and manual analysis for critical decisions like weld placement, weight targets, or NVH fixes.
Dataraft changes this fundamentally. Greywiz's no-code analytics platform connects directly to simulation outputs (LS-DYNA, ANSA), BOM spreadsheets, OBD logs, and text logs, enabling domain experts to build predictive models and optimizations without data science teams or coding. Here are four real-world engineering use cases showing Dataraft in action.
1. Spot Weld Optimization: 4 Fewer Welds in 50% Less Time
The Challenge: Spot weld placement in BIW (Body-In-White) structures is a high-stakes optimization balancing stiffness, crash performance, and manufacturing cost. Traditional DOE or solver-based methods require thousands of simulation runs and weeks of CAE time.
Dataraft Solution: Using Multi-Disciplinary Optimization (MDO) on simulation data from weld models:
Automated Sampling: Design-of-Experiments (DOE) with adaptive learning for faster convergence vs. traditional Latin Hypercube.
Lean Model Building: Noise elimination, feature ranking across multiple ML techniques (SHAP, permutation importance, etc.).
Multi-Objective Optimization: Stiffness targets vs. weld count/cost minimization using NSGA-II.
Results:
Achieved target stiffness with 4 fewer spot welds using 50% less time and 1/3rd the samples vs. previous methods.
Compounding ROI: At vehicle volumes of 100K+, weld reduction translates to millions in manufacturing savings.
Quick Convergence: Parameter sensitivity analysis pinpointed dominant factors (flange length, sheet thickness) for future designs.
This pattern repeats across BIW torsion, bending, lateral stiffness, frontal crash modeling, and NVH harness routing.
2. Curb Weight Prediction from Incomplete BOM Data
The Challenge: Early design phases lack complete CAD but need weight estimates for feasibility, packaging, and competitive benchmarking. Engineers manually roll up BOMs or extrapolate from past programs—error-prone and slow.
Dataraft Approach:
1. BOM Upload → Auto-feature engineering (material density × volume, component count, etc.)
2. Augment with competitor intelligence (public TDS, teardowns)
3. Regression modeling → Predict target curb weight
4. What-if analysis for "what if we switch aluminium for steel in closure panels?"
Results:
5% mean error on planned dimensions vs. actual vehicles.
Design Guidance: Weight predictions shaped early architecture decisions.
Benchmarking: Competitor data integration revealed 8–12% lightweighting opportunities vs. segment leaders.
Pro Tip: Combine with Dataraft's "text-to-query" for natural language BOM analysis: "Show me weight impact if we remove sound deadener from floorpan."
3. Clutch Failure Prediction from Road Test + OBD Data
The Challenge: Clutch pad failures trigger expensive warranty claims and erode JD Power VSA scores. Field data (OBD, telemetry) exists but lacks predictive models linking wear to driving patterns.
Dataraft Workflow:
Road Test Data (OBD channels) + Derived Features (shift aggression, load cycles)
↓
Automated Feature Engineering (rolling averages, gradients, interactions)
↓
Model Comparison (XGBoost, Random Forest, LSTM for time-series)
↓
Ensemble Model → Predict cycles-to-failure
Key Insights:
Mean error: 12% (targeting 10% with more cycles); scope for production deployment.
Dominant Drivers: Shift frequency under load, throttle modulation during gear changes, ambient temperature.
Actionable: Top 10% aggressive drivers consume clutches 2.5x faster—target telematics interventions.
Business Impact: Reduced warranty exposure by 15–20% through targeted design changes and driver coaching.
4. NVH from Customer Feedback: Text-to-Actionable Insights
The Challenge: Customer quality narrations arrive as free-text: "buzz from A-pillar at 80 kph," "squeak in door panel," "rattle from trunk." Converting to structured signals for NVH teams takes weeks of manual triage.
Dataraft Text Analytics:
1. NLP Processing → Extract entities (location, frequency, severity), sentiment
2. Topic Clustering → Buzz/Squeak/Rattle segmentation
3. Trend Analysis → Degradation patterns over warranty life
4. Correlation → Link to CAE modes, material specs
Results:
Warranty Period → Quality Degradation Signals
0-6 months: 12% buzz increase (A-pillar clips)
6-18 months: 28% squeak growth (door seals)
18-36 months: 41% rattle escalation (trunk mechanisms)
Impact:
JD Power Improvement: Fixed top-3 narrations drove +15 VSA points.
Route Cause Actions: Targeted clip redesigns, seal compounds, trunk latch tweaks.
Proactive: Early warranty signals triggered supplier 8D before field escalation.
Additional engineering wins using simulation + test data:
Use Case | Data Source | Outcome | Savings |
BIW Torsion/Bending | CAE + Road Tests | <2.5% mean error | 30% man-hours |
Dynamic Stiffness | Modal Testing + ML | ML outperformed physics-based predictions | Faster certification |
Crash Intrusion | Frontal Crash Sims | Global prediction via surrogate models | Reduced solver runs |
Dataraft Engineering Analytics: Platform Differentiators
What makes Dataraft uniquely suited for engineering workflows:
Pre/Post Integration: Direct connectors to ANSA, HyperMesh, LS-DYNA preprocessors/postprocessors.
Hybrid Physics-ML: Augment simulations with ML surrogates for 90% faster iterations.
Collaboration: Storefront UI with shared workflows, lessons-learned repository.
What-If Framework: Test design changes without re-running full sims.