Automotive Adhesive Quality & Operations Optimization Pipeline

📌 Executive Summary

This repository presents an end-to-end technical engineering framework for validating structural adhesive bonding systems in lightweight automotive vehicle construction (specifically Carbon Fiber Reinforced Polymer CFRP to E-Coated Aluminum/Steel joints).

By replacing unstructured testing logs with a 3NF Relational Data Architecture, automated Statistical Process Control (SPC), closed-loop 8D/PFMEA risk governance, and an executive Power BI Analytics Cockpit, this framework ensures regulatory compliance, eliminates administrative waste (Muda), and guarantees dynamic joint reliability.

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🔬 Core Engineering Workflow (5-Phase Pipeline)

1. Surface Pretreatment & Energy Optimization

• Evaluated contact angle ($\theta^\circ$) and surface energy ($\text{mN/m}$) across untreated, solvent-cleaned (IPA), and atmospheric plasma-treated CFRP substrates.
• Key Finding: Atmospheric plasma treatment raised surface energy to $52.0\text{ mN/m}$ ($\theta = 22.4^\circ$), surpassing the critical $45\text{ mN/m}$ threshold required for high-polar acrylic wetting.

2. Mechanical Tensile Testing (DIN EN 1465 / ISO 4587)

• Conducted continuous lap shear stress evaluation under standard laboratory conditions ($23^\circ\text{C}, 50%\text{ RH}$).
• Mapped force-displacement behavior, identifying peak loads ($\tau$) and failure modes (Cohesive vs. Adhesive).

3. Statistical Process Control & Capability (Minitab)

• Constructed Individual-Moving Range (I-MR) control charts and process capability histogram models.
• Process Capability Metrics:
  • Mean Shear Strength ($\bar{X}$): $2.477\text{ MPa}$
  • Process Capability Indices: $C_p = 1.75$, $C_{pk} = 1.70$ (exceeding the automotive $C_{pk} \ge 1.33$ benchmark)
  • Out-of-Control Violations: $0$

4. Root Cause Analysis & Risk Management (8D / PFMEA)

• Executed Ishikawa (6M) and 5-Why root-cause investigation for low-shear anomalies ($\tau < 1.50\text{ MPa}$), isolating pneumatic pressure drops in application rollers.
• Recalculated Risk Priority Numbers (RPN), successfully reducing high-risk steps from RPN 160 to 32 via automated pressure transducers and interlocks.

5. Power BI Analytics Cockpit Integration

• Built an interactive dashboard with customized DAX measures (Avg_Shear_Strength, Pass_Rate).
• Implemented KPI cards, dynamic failure mode breakdowns, and cross-filterable substrate performance bar charts.

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📊 Key Analytics & Visualization Preview

Visual Metric	Indicator / Chart	Value / Result
Overall Mean Shear Strength	Power BI KPI Card	2.22 MPa
Process SLA Pass Rate	Power BI KPI Card	75.0%
Cohesive Failure Proportion	Power BI Donut Visual	75.0% (6 / 8 Tests)
Optimized Plasma Strength	Minitab Capability	2.48 MPa
Process Capability ($C_{pk}$)	Minitab Distribution	1.70

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🎓 Academic & Author Context

• Author: Amid Sheikhi
• Academic Positioning: Master Student – Industrial Engineering (Wirtschaftsingenieurwesen), HAW Kiel
• Target Focus: Automotive Adhesive Application Engineering & Structural Bonding Validation
• Contact: LinkedIn | GitHub | amidsheikhi88@gmail.com