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Development of ML-Powered Classification System for Metal Scrap Recycling

softwaremind.com

Manufacturing

Environmental Services

Operational Inefficiencies in Scrap Metal Classification

Manual classification of scrap metal objects was time-consuming, error-prone, and unable to scale with increasing volumes. The client required automated, real-time classification to optimize recycling processes and improve material traceability.

About the Client

A branch of an international recycling group specializing in processing steel scrap and nonferrous metals to provide high-purity recycled materials for decarbonization initiatives.

Key Goals for AI-Driven Recycling Optimization

Develop a machine learning model for accurate photo-based object classification
Enable real-time processing via mobile integration
Centralize classification management through a web application

Core System Capabilities

ML model trained on AWS for object detection
Mobile app with camera integration for instant photo classification
Web dashboard for classification review, junkyard inventory management, and reporting

Technology Stack Requirements

.NET

Python

AWS Cloud Services

System Integration Needs

AWS S3 for image storage
Mobile device camera APIs
User authentication service

Operational Constraints

Scalable cloud infrastructure on AWS
Real-time classification latency <2s
Data encryption for compliance with environmental regulations

Anticipated Business Outcomes

Expected to reduce manual classification time by 70%, improve material purity verification accuracy to 98%, and enable scalable processing of 10,000+ daily recycling transactions while supporting sustainability goals through optimized resource recovery.