AI-Powered Smart Control Solution for Intelligent Recycling Automation
AAEON, a leading Taiwanese manufacturer of embedded computing platforms for AIoT. We provide customized and efficient solutions to meet diverse demands.
As recycling volumes continue to increase, contamination in raw waste streams has become a critical challenge, reducing material value, creating safety risks for operators, and potentially damaging sorting equipment.
By bringing AI intelligence closer to the equipment level, the AAEON BOXER-6647-MTH enables a smart control ecosystem for AI-enabled waste sorting applications. It provides real-time data processing and intelligent decision-making capabilities to accurately identify recyclable materials, detect hazardous items, and support automated sorting processes for efficient material separation and remanufacturing.
Features
- Intelligent Edge AI Processing for Real-Time Control
Powered by Intel® Core™ Ultra 7/5 processors with integrated CPU, NPU, and GPU acceleration, the BOXER-6647-MTH enables data preprocessing, computing tasks, and parallel AI workload execution within a single platform. This optimized architecture improves AI inference efficiency while reducing processing latency.
- Smart Device Connectivity and Control Integration
Supports reliable peripheral connectivity with cameras, sensors, displays, and industrial equipment, enabling seamless data acquisition, system monitoring, and machine control integration.
- Reliable Operation in Harsh Industrial Environments
Designed for demanding recycling environments, the BOXER-6647-MTH provides stable operation against challenges such as dust and debris, fluctuating power conditions, and vibration, ensuring continuous system reliability.
Applications
- AI Smart Recycling Sorting System
- AI-Powered Machine Vision System
- Automated Material Recovery Facility (MRF)
- Industrial Automation and Robotic Sorting
- Sustainable Smart Factory Applications
Benefits
- Enhanced Operational Efficiency
AI-driven smart control reduces manual inspection requirements, improves material identification speed, and enhances overall recycling productivity.
- Faster Decision Making with Lower Latency
Local AI inference enables rapid responses to detected objects, allowing sorting equipment to adjust operations without relying on cloud-based processing.
- Improved Safety and Risk Reduction
Real-time identification of hazardous materials helps protect operators, reduce equipment damage risks, and create safer recycling environments.
- Higher Resource Recovery Accuracy
AI-based recognition and automated separation improve recyclable material purity, increasing the value of recovered resources.
- Optimized AI Computing Efficiency
Dynamic workload distribution across CPU, NPU, and GPU improves computing utilization while reducing unnecessary power consumption.
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