Portfolio Project | Manufacturing Engineering + Data Science | FSAE-E Student Formula Prototype
This project develops a complete, data-driven manufacturing study for the body and chassis of a student electric formula prototype (FSAE-E class). It combines manufacturing process engineering with quantitative cost modelling, process efficiency analysis, and multi-objective optimization to identify the manufacturing configuration that achieves the best balance of cost, mass, and build time under realistic student-team constraints.
All data is either sourced from published engineering references (
[REF]), clearly documented assumptions ([ASM]), calculated results ([CALC]), or explicitly labelled synthetic datasets ([SYN]). No values are fabricated as real-world facts.
A student FSAE-E team must design and manufacture the body and chassis of an electric formula prototype with limited budget, limited equipment, and limited time. Optimising only one variable (e.g., lowest cost) produces unacceptable outcomes in the others (e.g., too heavy, too slow to build). A systematic, evidence-based approach is required to navigate the trade-offs between:
Cost ←→ Mass ←→ Manufacturing Time ←→ Structural Performance ←→ Manufacturability
| # | Question | Phase |
|---|---|---|
| RQ1 | What manufacturing processes are appropriate for this prototype? | Phase 3 |
| RQ2 | Which material provides the best balance of mass, cost, strength, and manufacturability? | Phase 4 |
| RQ3 | Which cost components contribute most significantly to total manufacturing cost? | Phase 5 |
| RQ4 | Where are the primary process bottlenecks, and what is their magnitude? | Phase 6 |
| RQ5 | Can optimization identify a configuration that simultaneously reduces cost, mass, and time? | Phase 7 |
| RQ6 | What is the recommended configuration, and what trade-offs does it accept? | Phase 8 |
Vehicle Architecture (Phase 2)
↓
Manufacturing Process (Phase 3) ← 28 detailed process stages
↓
Material Selection (Phase 4) ← MCDA + HAZ analysis + sensitivity
↓
Cost Modelling (Phase 5) ← Bottom-up: 8 cost categories
↓
Process Efficiency (Phase 6) ← VA/NVAN/NVA + Pareto defect analysis
↓
Multi-Objective Optimization (Phase 7) ← scipy.optimize + Pareto frontier
↓
Final Recommendation (Phase 8)
| Metric | Value |
|---|---|
| Vehicle concept | Single-seat open-wheel FSAE-E formula prototype (SEFP-01) |
| Chassis architecture | Steel tube space frame (AISI 1018 + AISI 4130 hybrid) |
| Body panels | GFRP (E-glass/epoxy) hand layup |
| Baseline total cost | USD 4,873 [CALC] |
| Optimized total cost | USD 3,226 [CALC] |
| Cost reduction | −33.8% [CALC] |
| Baseline mass (chassis + body) | 43.9 kg [CALC] |
| Optimized mass | 41.1 kg [CALC] |
| Mass reduction | −6.4% [CALC] |
| Baseline manufacturing time | 178.9 hr [ASM] |
| Optimized manufacturing time | 104.8 hr [CALC] |
| Time reduction | −41.4% [CALC] |
| Process efficiency (VA ratio) | 38.9% baseline → 66.4% optimized [CALC] |
| Primary bottleneck | Body panel mold workflow — 73.8 NVAN hr (41.2% of total time) [CALC] |
| Dominant strategy | Mold reuse (γ=1) — appears in 100% of Pareto-optimal solutions [CALC] |
| Labor % of total cost | 72.7% — largest single cost driver [CALC] |
Key finding: Material specification changes (e.g. GFRP → CFRP) affect only ~5% of total cost. Labor hour reduction — through mold reuse, process planning, and defect prevention — is the primary lever for cost and time improvement.
| Category | Tools |
|---|---|
| Programming | Python 3.x |
| Data manipulation | pandas, NumPy |
| Optimisation | scipy.optimize (SLSQP) |
| Visualisation | Matplotlib, Seaborn, Plotly |
| Statistical analysis | scipy.stats, statsmodels |
| Spreadsheet | Excel / CSV |
| CAD (recommended) | Autodesk Fusion 360 (free education licence) |
| Documentation | Markdown, GitHub |
| References | SAE, ASM Handbook, AWS Welding Standards, FSAE 2024 Rules |
ev-manufacturing-analysis/
│
├── README.md
│
├── 01_problem_definition.md
├── 02_vehicle_architecture.md
├── 03_manufacturing_process.md
├── 04_material_selection.md
├── 05_cost_analysis.md
├── 06_process_efficiency.md
├── 07_optimization.md
├── 08_recommendation.md
│
├── data/
│ ├── raw/
│ │ ├── bom_baseline.csv ← Bill of Materials with mass/cost [CALC/ASM]
│ │ ├── bom_cost_baseline.csv ← Per-component cost dataset [CALC/SYN]
│ │ ├── material_properties.csv ← Material data table [REF/ASM]
│ │ ├── process_times.csv ← Stage-level time data [ASM]
│ │ └── defect_data_synthetic.csv ← Synthetic defect dataset [SYN]
│ └── processed/
│ ├── scenario_comparison.csv
│ └── pareto_frontier.csv
│
├── notebooks/
│ └── manufacturing_analysis.ipynb ← Main analysis notebook
│
├── src/
│ ├── cost_analysis.py
│ ├── process_analysis.py
│ ├── material_selection.py
│ └── optimization.py
│
├── figures/
│ ├── material_sensitivity_heatmap.png
│ ├── cost_analysis.png
│ ├── labor_rate_sensitivity.png
│ ├── process_efficiency.png
│ ├── value_added_analysis.png
│ └── optimization_results.png
│
├── cad/ ← CAD models (SolidWorks / Fusion 360)
│ ├── chassis/
│ ├── body/
│ └── technical_drawings/
│
└── references/
├── phase1_references.md
└── assumptions_register.csv
This project rigorously distinguishes four data types throughout all documents:
| Tag | Meaning |
|---|---|
[REF] |
Sourced from a published reference (SAE, ASM Handbook, AWS, FSAE Rules, etc.) |
[ASM] |
Engineering assumption with documented rationale |
[CALC] |
Derived mathematically from REF or ASM values |
[SYN] |
Synthetic/simulated data for analytical demonstration — never presented as real data |
No synthetic data is presented as real manufacturing data from any actual vehicle or team.
- All process times are engineering assumptions
[ASM]— not measured from a real build - Defect/quality data is entirely synthetic
[SYN]— not sourced from real inspection records - Material prices are 2023–2024 online-supplier estimates
[ASM] - No FEA structural validation performed (flagged as future work)
- No CFD aerodynamic analysis performed
- Labor rates reflect student volunteer context — institutional overhead not included
- CAD geometry described but not generated by this study
- Optimisation results are model outputs, not experimentally verified
| Priority | Item |
|---|---|
| High | Collect real process times from an actual build cycle |
| High | Replace synthetic defect data with real inspection records |
| High | Obtain real supplier quotations for material pricing |
| Medium | FEA structural analysis of critical chassis joints (Fusion 360 Simulation or Abaqus) |
| Medium | CFD aerodynamic surface analysis of body panels |
| Medium | Physical prototype testing (torsional stiffness, weld destructive testing) |
| Medium | Digital twin of manufacturing process (Arena / Siemens Plant Simulation) |
| Low | Extend optimisation to full vehicle (suspension, powertrain) |
| Low | Predictive maintenance model for workshop equipment |
| Low | Lifecycle cost analysis (manufacturing + operation + disposal) |
Developed as a portfolio project for application to an EV student engineering team, Manufacturing Division.
Methodology standard: Engineering analytics case study — not a machine learning project. Priority: Technical credibility > visual appearance. Reproducible reasoning > impressive claims.
All monetary values in USD. All mass values in kg. All time values in hours unless otherwise stated.