Fatigue Agent — Technical Pitch (Slide Content)¶
Source:
Architechture & Research/Fatigue Agent/Pitch/FatigueAI Technical Pitch v2.pptx(v2, February 2026) Status: Active — use for investor/customer conversations Audience: Engineering managers, stress analysts, industrial equipment manufacturers
This page captures the full content of the Fatigue Agent technical pitch presentation (v2). The pitch story runs from problem → context → approach → architecture → governance → workflow comparison → validation → pilot CTA.
Slide 1 — Title¶
Automated Fatigue Post-Processing for Industrial Equipment & Machinery
An agent-based approach to fatigue life prediction from existing FE results — solver-agnostic, deterministic, traceable
February 2026
Slide 2 — Problem: Fatigue Post-Processing Remains Largely Manual¶
Most industrial equipment manufacturers perform static FEA but handle fatigue assessment manually — or skip it entirely.
The current manual workflow: 1. Extract nodal stresses from FE results 2. Identify critical locations manually 3. Look up S-N data from standard (PDF/book) 4. Rainflow count load history in Excel 5. Calculate damage per Miner's rule 6. Compile report in Word
The result: - 4–8 hours per analysis - 0% traceability - ±30% analyst variance
Slide 3 — Context: Your Engineering Environment¶
Company profile: Machine builders, conveyor OEMs, packaging line manufacturers — Mittelstand / SME
| Dimension | Typical state |
|---|---|
| Team size | 1–3 simulation engineers per company |
| Purchasing | Engineering-led decisions |
| Solvers | OptiStruct, Nastran, Abaqus, Ansys Mechanical |
| Structures | Welded steel/aluminium frames, brackets, sheet-metal assemblies |
| Load profiles | Deterministic duty cycles — bending, tension, combined loading |
| Current practice | Static analysis with safety factor 2–5×; often no explicit fatigue check |
Slide 4 — Problem: Consequences of Missing Fatigue Data¶
Without systematic fatigue assessment, engineering teams face two failure modes:
Under-design: - Field failures discovered by customers - Warranty claims, production downtime, reputational risk - Reactive rather than preventive engineering
Over-design: - Safety factors of 3–5× without basis - Excess material cost (+30–40%), unnecessary weight - Conservatism masking as engineering rigour
Slide 5 — Approach: Agent-Based Fatigue Post-Processing¶
Operates on existing FE results — no new solver, no changes to your modelling process.
Post-processing only: Works downstream of your existing solver. Reads .op2, .h3d, .odb, .rst result files directly. No licence dependencies, no solver changes.
Deterministic math: Palmgren-Miner damage, rainflow counting, S-N interpolation. No ML predictions on fatigue life — identical inputs produce identical outputs.
Full traceability: Every output links to specific inputs — S-N curve, load case, extraction point, standard clause. Auditable by design.
Slide 6 — Workflow: Processing Pipeline¶
FE result file (.op2, .h3d, .odb, .rst)
↓ Result parser
Stress extraction at candidate locations
↓ Hotspot detection
Critical location ranking
↓ Load cycle processing
Rainflow counting (ASTM E1049)
↓ Damage calculation
Palmgren-Miner (FKM / IIW / Eurocode 3)
↓ Report generation
Traceable PDF with input hashes, S-N data, method rationale
Standards applied: FKM Guideline · IIW Recommendations · Eurocode 3 · ASTM E1049 (Rainflow) · Goodman / Gerber / FKM R-ratio
Slide 7 — Technical Depth: Standards-Based Fatigue Data¶
- S-N curves per IIW FAT classification (steel, aluminium weld classes)
- Duty cycle load history with peak detection and sequence counting
- Rainflow counting matrix per ASTM E1049
- Built-in material library covering FKM and IIW FAT class data
Slide 8 — Analysis Output: Hotspot Identification and Damage Ranking¶
- Stress field with auto-detected critical locations
- Cumulative damage ranked by severity
- Top 20 locations ranked by damage, with confidence flags
- Sensitivity analysis for parameter changes (material, load amplitude, safety factors)
Slide 9 — Architecture: System Architecture¶
Interface Layer
Web UI (React) · File upload · Parameter config · Report download
↓
Agent Layer
LLM orchestrator · Method selection · Input validation · RAG on standards
↓
Compute Engine
Deterministic Python (NumPy/SciPy) · Result parsers (pyNastran, h5py) · Rainflow · Miner's rule
Key constraint: The AI layer handles workflow orchestration and method selection only. All fatigue calculations are deterministic — identical inputs always produce identical outputs.
Slide 10 — Governance: Engineering Governance¶
Every analysis run produces a complete audit record. The system enforces method boundaries to prevent invalid configurations.
| Control | Detail |
|---|---|
| Method approval | Only FKM / IIW / Eurocode methods enabled. No custom or unvalidated approaches. |
| Parameter bounds | State machine enforces valid ranges. Rejects out-of-scope inputs before computation. |
| Traceability | Report includes input file hashes, S-N curve IDs, method rationale, extraction coordinates. |
| Reproducibility | Deterministic compute — same inputs produce identical outputs regardless of run time. |
| Audit log | Timestamped record of all agent decisions, method selections, and user overrides. |
Slide 11 — Compatibility: Supported Formats and Data¶
| Format | Extensions | Phase |
|---|---|---|
| OptiStruct | .op2, .h3d |
Phase 1 (priority) |
| Nastran | .op2 |
Phase 1 (via pyNastran — shared format) |
| Abaqus | .odb |
Phase 2 (via Abaqus Python API) |
| Ansys | .rst |
Phase 2 (via pyMAPDL or dpf-core) |
| Load data | CSV, time series | Phase 1 (duty cycles, vibration spectra) |
| Material data | Built-in library | Phase 1 (FKM / IIW FAT classes — steel, aluminium) |
Slide 12 — Comparison: Workflow Comparison¶
| Dimension | Manual process | Agent-based |
|---|---|---|
| Cycle time | 4–8 hours | ~15 minutes |
| Consistency | Analyst-dependent | Deterministic |
| Traceability | Implicit | Explicit — documented |
| Method selection | Manual lookup | Auto-selected |
| Hotspot ID | Visual inspection | Ranked by damage |
| Reporting | Manual Word | Auto-generated PDF |
| Specialist required | Yes | No — guided workflow |
Slide 13 — Validation: Three-Phase Validation Approach¶
Validated against known benchmarks and pilot partner data before any production use.
Phase 1 — Benchmarks: Validate core engine against published analytical solutions. IIW/FKM benchmark problems. ASTM E1049 reference signals.
Phase 2 — Pilot data: Compare agent outputs against existing manual assessments from pilot partners. Same FE results, same load cases.
Phase 3 — Field correlation: Correlate predictions with actual field failure data. Track prediction accuracy. Build confidence intervals.
Slide 14 — CTA: Pilot Offer¶
We are looking for 3–5 engineering teams in the industrial equipment sector to validate this approach against real analysis workflows.
What a pilot involves: - You provide representative FE results and load histories - We run the agent and compare against your existing assessments - Joint review of results, method selection, and report quality - 3–4 month validation period — no cost during pilot
Contact: hello@rapiddraft.com
Pitch Notes¶
On the "deterministic" claim: This is the most important technical credibility point. The LLM never touches fatigue numbers. It orchestrates the workflow and selects methods. All Miner's rule calculations, rainflow counting, and S-N interpolation happen in deterministic Python. This is not marketed as AI fatigue analysis — it is fatigue analysis with an AI workflow layer.
On competition: FEA tools (ANSYS Mechanical, OptiStruct) have fatigue modules, but they require the analyst to set up every parameter. Dedicated fatigue tools (nCode, fe-safe, FEMFAT) are expensive (€10k–50k+) and targeted at automotive OEMs. The Fatigue Agent targets the 1–3 analyst team at a Mittelstand company that cannot justify those tools.
On adoption path: No changes to existing solver, no new CAD license, no IT project. The engineer exports their FE results, uploads to the agent UI, and gets a traceable fatigue report. This is designed to be a day-one workflow.