Computer vision analyzing ore fragments in real time as they move along a mining conveyor belt, while engineers review particle-size distribution and identify oversized material before it reaches downstream grinding equipment.
心を動かす会話

Ore Size Intelligence in Motion

Automating real-time granulometry on conveyor belts to reduce jams and stabilize material entering the grinding process.
会議のスケジューリング
The conveyor does not pause while material quality changes.
WHY THIS "GRANULOMETRY" CHALLENGE?
Oversized rocks, excessive fines, and changing distributions move downstream at belt speed. Without continuous measurement, deviations may only become visible when they reach a screen, feeder, chute, or mill.
主なメリット
Make the conveyor a live process measurement

Computer vision turns moving material into continuous granulometry, giving operations earlier evidence of feed conditions before they become equipment problems.

チェックアイコン
Measure without stopping the belt

Maintain granulometry while material stays in motion.

チェックアイコン
Catch oversize upstream

Identify abnormal fragments before they disrupt downstream equipment.

チェックアイコン
See feed variability as it develops

Detect changing material conditions entering the mill.

チェックアイコン
Apply one logic across the operating period

Keep segmentation and classification consistent over time.

NTTデータのサポート体制は？

01
Design the measurement point

Define camera position, viewing area, image quality, and operating conditions.

Mining specialist positioning and configuring a computer-vision camera above a moving ore conveyor to establish the optimal viewing area for continuous particle-size measurement.
02
Separate ore from equipment

Segment mineral fragments and isolate visible material from surrounding structures.

Computer-vision camera observing mineral fragments moving along a conveyor belt, enabling the system to distinguish visible ore from the belt and surrounding industrial structures
03
Turn images into granulometry

Estimate size distribution and generate granulometry curves.

Computer-vision system analyzing mineral fragments moving on a conveyor belt and converting visual measurements into a real-time particle-size distribution for mining operations.
04
Detect threshold breaches early

Identify material above defined limits and notify the relevant team.

Mining operations specialists reviewing a computer-vision alert after oversized ore is detected on a moving conveyor, enabling the relevant operational team to respond before the material reaches downstream equipmen
05
Connect measurement to operating response

We don’t stop at visualization. We provide information that supports automatic or operator-approved feeder adjustment.

Machine-vision camera measuring mineral fragments on a moving conveyor as ore flows from a feeder, illustrating how continuous granulometry can support adjustments to the material feed process.
実証済みの効果
Every image becomes a measurement. Every deviation becomes visible sooner.
重要な結果
Continuous granulometry creates earlier visibility of feed conditions and downstream risk.
チェックアイコン
Reduced jam-related downtime

4% reduction reported in an anonymized use case.

チェックアイコン
Maintained live size visibility

Real-time granulometry across coarse and fine distribution.

チェックアイコン
Identified oversize earlier

Abnormal fragments detected before downstream disruption.

チェックアイコン
Stabilized the mill-feed picture

Better visibility supported more consistent material conditions.

Make every conveyor image part of the process-control loop.

Keep material variability visible before it disrupts production.

話しましょう
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天然資源
自律運転
Implementing the Agentic IROC
Deploying Physical AI in controlled waves so the operation builds trust, demonstrates value, and advances toward cross-domain orchestration without compromising safety or continuity.
Mining engineers transforming an approved autonomous operating model into production-ready engineering packages covering AI agents, data, integrations, authority, cybersecurity, testing, and commissioning.
天然資源
Engineering the Agentic IROC
Turning an approved autonomous operating model into buildable, testable, cybersecure engineering packages for agents, data, integrations, authority, and commissioning.
Integrated mining operations center using Physical AI to connect mine, plant, maintenance, logistics, energy, water, and safety operations while specialized AI agents coordinate recommendations under human supervision.
天然資源
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From Recommenders to an Agentic IROC
Transforming the integrated operations center into a Physical AI system that understands the end-to-end operation, coordinates specialized agents, and turns insight into governed action.
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Using computer vision to detect unsafe vehicle behavior and expose time-critical interactions between road and rail.
Motion analytics monitoring a suspended industrial load during a lifting operation at a mining site, tracking speed, oscillation, amplitude, and trajectory while an operator supervises the operation.
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Predictive Lifting Safety
Using motion analytics to detect abnormal speed and oscillation in suspended loads before lifting risk escalates.
Automated visual inspection system analysing copper cathodes on a production line while an industrial operator monitors quality data in a modern mining processing facility.
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AI-Assisted Copper Cathode Inspection
Embedding real-time visual quality control into cathode production to detect defects, reduce errors, and strengthen traceability.
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