Quick Summary
- ▸ CodeBranch's dedicated development team worked on this project, which was an AI image recognition system that extracted the dimensions of a metal plant's waste and stored them in an inventory for use in future projects.
- Delivered a system based on genetic algorithms that recognises the shape and dimensions of scrap metal pieces.
- Enabled the metal company to optimize resources and save money by systematically reusing waste material in future projects.
Overview
The system was developed for a sheet metal plant. The main objective was to create a system that catalogs the inventory of waste based on shapes, dimensions, gauges, and materials. To create the inventory, the waste was placed on a table to be photographed. Using an AI image recognition system, the dimensions were extracted and the waste was stored in the inventory system. Each time a new project was started, the system — based on genetic algorithms — checked if there were pieces in the waste inventory that met the specific needs of different parts of the products to help the recycling process. In addition, if a new sheet was to be used, the system suggested how the sheet should be cut to reduce waste as much as possible.
Industries
Services Provided
- Custom Software Development
- AI Development
Approach
The technology used in this project included PolyK for polygon shape computation, Genetics-JS for running genetic optimization algorithms, JavaScript for application and UI logic, and Python for AI image recognition and backend services. Waste pieces were photographed on a designated table and processed by the AI model to extract dimensional data automatically. The genetic algorithm then handled the matching and cutting optimization logic. This project involved 4 developers (2 senior and 2 semi-senior), 1 QA expert, and 1 UI/UX designer.
Results
- Delivered a system based on genetic algorithms that recognises the shape and dimensions of scrap metal pieces.
- Enabled the metal company to optimize resources and save money by systematically reusing waste material in future projects.
- Reduced raw material costs by matching existing scrap inventory to new project requirements before ordering new stock.
- Minimized future waste generation by providing AI-driven cut optimization suggestions for new metal sheets.
