FAASST — Machining Speed & Feed API (SaaS)

🚀 FAASST is a modern SaaS API that generates optimized cutting parameters for milling and turning tools. It turns messy manufacturer catalog data into reliable, production-ready recommendations.

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1. Overview

This service provides a single source of truth for:

• Cutting speed (V_c)
• Feed per tooth (f_z)
• RPM and feed rate
• Surface speed and chip load adjustments

Built on:

• ISO 13399 tool geometry
• ISO 513 material groups
• Manufacturer catalogs as the authoritative data source
• Physics-based machining logic

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2. Core Data Architecture — Golden Record

All scraped data is normalized into a standardized schema for interoperability with CAM/PLM systems.

A. Tool Geometry (ISO 13399)

Key	Description	Standard Field
DC	Cutting diameter	hand_tool_diameter
ZEFP	Effective cutting edges	flute_count
RE	Corner radius	tip_radius
FHA	Flute helix angle	helix_angle
LU	Usable length	reach_length

B. Material Groups (ISO 513)

We map proprietary material codes (e.g. P2.1) to universal groups:

• P — Steel
• M — Stainless Steel
• K — Cast Iron
• N — Non-ferrous / Aluminum
• S — HRSA / Titanium
• H — Hardened Steel

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3. Scraping Pipeline

Manufacturer catalogs are often unstructured and dynamic, so the pipeline uses multiple stages to ensure accuracy.

• Orchestration: Node.js with Playwright/Puppeteer for JS-heavy pages
• Extraction: Vision-capable LLM (Gemini 1.5 Pro / GPT-4o) to parse images and tables into structured JSON
• Validation: Cross-check extracted values against manufacturer SKU data

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4. Backend Strategy — Supabase + Logic

The backend uses Supabase/PostgreSQL with pgvector for advanced material matching.

Primary tables

• dim_tools — static tool geometry per SKU
• fact_cutting_data — raw catalog ranges for V_c and f_z
• ref_materials — vector-enabled mapping for user materials to catalog groups

Physics-driven logic

The API calculates more than raw numbers:

• Radial chip thinning adjustments when a_e < 50%
• Swiss turning support for guide bushing / reverse-turning constraints
• Unit-agnostic outputs: metric and imperial support (m/min, mm/rev, SFM, IPT)

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5. API Draft

POST /v1/calculateInput

Request example:

{
  "sku": "ABC-123",
  "material": "4140 Steel",
  "hardness_hrc": 28,
  "strategy": {
    "type": "side_milling",
    "ae": 1.5,
    "ap": 12.0
  }
}

Response example:

{
  "recommendation": {
    "speed_rpm": 4500,
    "feed_ipm": 62.5,
    "surface_speed_sfm": 589,
    "feed_per_tooth": 0.0035,
    "chip_load_adjusted": true,
    "logic_notes": "Increased feed by 15% due to radial chip thinning."
  }
}

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6. Project Roadmap

Read the roadmap and actionable TODO list in TODO.md.

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7. Team & Standards

• Lead Engineer: Shane V Waid
• Standards: ISO 13399 / GTC compliant

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8. Why FAASST?

FAASST makes tooling data searchable, consistent, and ready for automated machining decision-making.

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9. Getting Started

1. Clone the repo

git clone https://github.com/your-org/FAASST.git
cd FAASST

2. Install dependencies

npm install

3. Configure environment

Create a .env file with your Supabase URL, service key, and any scraping credentials.

4. Run the API locally

npm run dev

5. Test the endpoint

Send a POST request to /v1/calculateInput with the example payload in the API Draft section.

6. Extend the pipeline

Start by adding one manufacturer scraper, then connect the extracted JSON into the Supabase schema.