Commercial use / consulting for 2DNesting

[ApexGBL]

Hello @lryan599,

I am working on a CNC cabinet production workflow and I found your 2DNesting project very interesting.

I would like to ask two things:

1. Would you be open to share some ideas or guidance about the design of your nesting engine (overall architecture, data structures, algorithm choices, etc.)?
2. Would you consider any form of commercial cooperation (for example: paid consulting, developing a DLL/API wrapper, or providing a separate license for using parts of your code in a commercial C# application)?

At the moment I am exploring options to build a PartNester-like nesting tool in C#, and your project looks like a very good reference.

If you are interested, please contact me here on GitHub or via email:

• Name: Nuri
• Company: MacroDesign
• Email: alderanuri@gmail.com
• (Optional) +46735250309 WhattsApp

Thank you for your time and for sharing your work open source.

[lryan599]

Hi Nuri,

I'm delighted to hear about your interest in this repository!

First and foremost, please note this key point:

This project is primarily designed for practice and learning purposes. Exercise caution if you intend to use it in actual production environments.

Unfortunately, since this repository relies on CGAL (the Computational Geometry Algorithms Library), it is bound to the GPL 3.0 License. And at present, I have no plans for commercialization.

The problem addressed here is the 2D Irregular Strip Packing Problem. For more detailed information, you can search this keyword on GitHub or in academic journals. For example, Sparrow is an excellent related repository, which also has its own library called Jagua-rs, can be use for commercialization directly.

In 2D Packing Problems—especially for irregular (polygon) scenarios—two core challenges stand out:

• How to define the overlap between two polygons?
• How to minimize such overlap from a global perspective?

Regarding the first question, many solutions leverage a geometric tool called the No-Fit Polygon (NFP)—feel free to search for it to learn more.

As for the second, most approaches adopt meta-heuristic algorithms (e.g., Genetic Algorithms/GA) to optimize overlap in the layout. Notably, this repository has not yet implemented any meta-heuristic algorithms. You can read this paper for more infomation: https://doi.org/10.1016/j.ejor.2013.06.020. 2DNesting is a lightweight and improved implementation of the algorithm presented in this paper.