20/03/2026
Somkid Laonittharakul PhD’s RCFA (Root Cause Failure Analysis) framework**, which integrates **macroscopic design**, **microscopic materials science**, and **virtual simulation (“bridge”)** into a unified investigation approach
RCFA Framework (Somkid Laonittharakul PhD)
This framework is built on **three interconnected domains**:
# # 1) Design Analysis (Macroscopic Level)
Focus: **System-level behavior, engineering intent, and real-world operation**
# # # Key Elements
* **Functional Requirements**
* What the system/component was *intended* to do
* **Load & Stress Conditions**
* Static, dynamic, thermal, cyclic loads
* **Geometry & Configuration**
* Shape, dimensions, tolerances
* **Failure Modes**
* Fatigue, overload, creep, buckling, wear
* **Standards & Safety Factors**
* Codes, design margins, assumptions
-Typical Tools
* FMEA (Failure Mode and Effects Analysis)
* Fault Tree Analysis (FTA)
* Engineering drawings & CAD review
* Operational history review
-Output
* Identification of **design weaknesses**
* Mismatch between **design assumptions vs actual conditions**
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# # 2) Materials Analysis (Microscopic Level)
Focus: **Material behavior, defects, and degradation mechanisms**
# # # Key Elements
* **Microstructure Examination**
* Grain size, phases, inclusions
* **Fractography**
* Crack origin, propagation patterns
* **Material Properties**
* Hardness, strength, toughness
* **Chemical Composition**
* Alloy correctness, contamination
* **Damage Mechanisms**
* Corrosion, fatigue cracking, embrittlement, creep
# # # Typical Tools
* Optical Microscopy (OM)
* Scanning Electron Microscopy (SEM)
* Energy Dispersive Spectroscopy (EDS)
* X-ray Diffraction (XRD)
* Hardness testing, tensile testing
# # # Output
* Root cause at **atomic / microstructural level**
* Identification of **defects, impurities, or degradation**
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# # 3) Simulation (Virtual Bridge)
Focus: **Connecting macro design with micro material behavior**
This is the **core innovation** of the framework — the *“virtual bridge”*.
# # # Purpose
* Validate hypotheses from Design & Materials
* Reproduce failure in a controlled digital environment
* Quantify cause-effect relationships
# # # Key Methods
* **Finite Element Analysis (FEA)**
* Stress, strain, deformation
* **Computational Fluid Dynamics (CFD)** (if applicable)
* **Thermal Simulation**
* **Fatigue Life Modeling**
* **Crack Propagation Simulation**
# # # Inputs
* Geometry from design analysis
* Material properties from microscopic analysis
* Real operating conditions
# # # Outputs
* Stress concentration zones
* Failure initiation points
* Life prediction vs actual failure
* Scenario testing (“what if” conditions)
# 🔗 Integration Logic (The “RCFA Triangle”)
The strength of this framework is **cross-validation**:
* **Design → Simulation**
* Verify whether design can realistically fail under given loads
* **Materials → Simulation**
* Use real material properties instead of ideal assumptions
* **Simulation → Design & Materials**
* Confirm or reject hypotheses
👉 The “virtual bridge” ensures:
> No conclusion is accepted unless **all three domains agree**
# 🧠 Root Cause Identification Strategy
# # # Step-by-Step Flow
1. **Observe failure (macroscopic)**
2. **Characterize fracture/material (microscopic)**
3. **Build simulation model**
4. **Iterate until alignment**
* Design + Materials + Simulation = consistent failure mechanism
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# ⚠️ Key Principles of Somkid’s RCFA Approach
* **No single-discipline conclusion is sufficient**
* Avoid:
* “Design-only blame”
* “Material-only blame”
* Emphasize:
* Interaction effects (e.g., poor design + marginal material)
# 📊 Example (Conceptual)
**Failure: Shaft fracture**
* **Design level**
* Sharp corner → stress concentration
* **Material level**
* Inclusion defect → crack initiation
* **Simulation**
* Shows stress peak exactly at inclusion location
✅ Root cause:
> Combined effect of **design stress concentration + material defect**
# 🧩 Why This Framework Matters
* Reduces **false root cause conclusions**
* Prevents **recurring failures**
* Supports:
* Engineering redesign
* Quality control improvement
* Predictive maintenance
