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🧪 Taguchi.Ai v2.0 Pro

Precision Engineering meets AI Intelligence.
Transform your experimental chaos into scientific certainty with the world's most intuitive Taguchi Design of Experiments (DOE) workspace.

Vite React Scientific Computing


🌟 Vision

Engineering is often a battle against variance. Taguchi.Ai was built to give researchers, students, and industrial engineers a professional-grade "Minitab alternative" that lives in the browser.

Whether you are optimizing a chemical reaction, tuning a 3D printer, or engineering aerospace components, Taguchi.Ai helps you find the Robust Optimum with the fewest possible experiments.


🚀 The Three-Step Workflow

1. 🏗️ Design Mode (The Planner)

Forget manual matrix lookups. Define your control factors, name your levels (e.g., 100°C, 200°C), and let the engine select the mathematically perfect Orthogonal Array ($L_4$, $L_9$, $L_{18}$, etc.).

  • Result: A professional Hardware Map (Experiment Plan) that you can export to Excel or print for the lab.

2. 🧪 Lab Execution

Take your exported plan to the workbench. Conduct your trials following the scientific roadmap provided. No guessing, no wasted resources—just targeted data collection.

3. 📊 Analysis Mode (The Oracle)

Paste your results directly into the dash. Taguchi.Ai instantly performs the complex math:

  • S/N Ratio Analysis: Measure robustness against noise.
  • ANOVA Decomposition: See which factor truly "moves the needle."
  • Optimal Prediction: Get the exact "Secret Recipe" for your product's peak performance.

🧠 Scientific Capabilities

Feature Description
Orthogonal Arrays Authenticated tables for $L_4, L_8, L_9, L_{12}, \dots L_{27}$.
S/N Ratios Support for Smaller-is-Better, Larger-is-Better, and Nominal-is-Best.
Full ANOVA Calculation of $SS, df, MS, F$-Ratio, and % Contribution.
Mixed Levels Advanced mapping for mixed resolution designs (like $L_{18}$).
Excel Export One-click CSV generation for professional documentation.

🔌 Headless Math API

Taguchi.Ai is built with a decoupled architecture. The core mathematical engine (taguchiEngine.js) is a standalone, headless library.

  • Portable: Import the engine into any JavaScript/Node.js project.
  • Universal: Can be used to power IoT sensors, backend research servers, or mobile apps.
  • Zero Dependencies: The math core is pure vanilla JS, ensuring it stays fast and future-proof.

🛠️ Tech Stack

  • Engine: Custom research-grade JavaScript Mathematics Core (The "Brain").
  • UI: Apple-inspired Glassmorphism with React & Vanilla CSS.
  • Charts: Dynamic Main Effects plots via Recharts.
  • Icons: Lucide-React Precision suite.

📦 Getting Started

  1. Clone the Repo
    git clone https://github.com/yourusername/taguchi-ai.git
  2. Install & Run
    npm install
    npm run dev
  3. Launch Open http://localhost:5173 and start optimizing.

🎓 For Researchers

Taguchi.Ai follows the G. Taguchi (1986) methodology for Robust Parameter Design. The Signal-to-Noise (S/N) formulas and ANOVA decompositions are verified against standard DOE benchmarks to ensure publication-ready accuracy.


Built with ❤️ for the Global Engineering Community.
Taguchi.Ai — Engineering the Future, One Run at a Time.

About

A research-grade backend engine for Taguchi Design of Experiments (DOE) and Robust Engineering Optimization, built for mechanical, aerospace, manufacturing, and reliability system analysis. This project implements the mathematical core behind professional tools like Minitab, enabling statistically sound experimental DOE & parameter optimization.

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