Real-time quality intelligence for the fiber draw process.
Inline computer-vision inspection detects diameter variance, bubbles, and coating defects at production-line speed, giving optical fiber manufacturers continuous visibility into quality while the fiber is being drawn.
In short
Aeologic built an inline computer-vision inspection system integrated into the fiber draw tower to continuously detect diameter variance, internal bubbles or voids, and coating defects at production speed. Edge AI inference processes inspection data locally, while PLC/SCADA integration enables real-time alerts and corrective action and centralized analytics provides complete defect traceability.
- Client Enterprise optical fiber & cable manufacturer
- Problem Offline sampling could miss defects occurring during high-speed fiber drawing
- Solution Inline vision + edge AI + PLC/SCADA feedback
- Deployment Edge-deployed on-premise with cloud analytics and reporting
Manual, offline quality checks couldn't keep up with fiber moving through the draw tower at production speed.
Optical fiber is drawn at extremely high line speeds, often several meters per second, as molten glass preform is pulled, cooled, and coated in a single continuous process. At this speed, defects such as diameter variance, internal bubbles or voids, and coating inconsistencies can occur in fractions of a second and are effectively invisible to manual, sample-based quality checks. Traditional QC relies on periodic offline sampling and end-of-spool testing, which means defective sections are often identified only after the fiber has already been drawn, spooled, or shipped downstream. This leads to elevated scrap and rework rates, costly fiber breaks during the draw itself, inconsistent attenuation performance, and warranty exposure once defective fiber reaches cable manufacturing or field deployment. The manufacturer needed a way to inspect every meter of fiber, in real time, without slowing the draw process.
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Periodic offline sampling could miss defects occurring between inspection checkpoints.
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Diameter variance, bubbles, voids, and coating issues could remain undetected until downstream testing.
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Defects discovered late increased scrap, rework, fiber breaks, and warranty exposure.
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Quality teams needed continuous inspection without slowing the draw process.
What the inspection system had to achieve.
Detect diameter variance, bubbles, and coating defects in real time during the fiber draw process, not after the fact.
Reduce dependence on manual, sample-based quality checks that cannot keep pace with draw-line speed.
Minimize scrap, rework, and fiber breaks by catching defects at their point of origin.
Feed real-time inspection signals back into the draw-tower control loop to enable immediate corrective action.
Provide complete defect traceability, timestamped and mapped to exact spool and fiber-length position.
Build a modular inspection architecture that can be replicated across multiple draw towers and plants.
Continuous inline vision and edge intelligence embedded directly into the fiber draw process.
High-speed inline vision
High-resolution line-scan and area-scan cameras continuously image the fiber around the draw tower while it moves through the production line at full speed.
Real-time defect intelligence
Edge AI measures fiber characteristics and classifies diameter variance, bubbles, voids, and coating irregularities as they occur.
Closed-loop production feedback
Inspection signals are delivered to PLC/SCADA systems for real-time alarms and automatic or operator-guided corrective action.
Continuous diameter measurement
Measurement algorithms compare fiber diameter continuously against defined tolerance bands and flag deviations immediately instead of waiting for end-of-spool sampling.
Bubble and void detection
Computer-vision models trained on defect imagery identify internal bubbles and voids in the glass fiber before they can propagate into downstream failures or breaks.
Coating defect classification
The system identifies coating thickness inconsistency, eccentricity, and surface irregularities and classifies each defect type for targeted production response.
Edge AI inference
Detection and classification models run on edge compute located at the production line, avoiding cloud round-trip latency while maintaining inspection performance.
Industrial IoT integration
A dedicated integration layer connects inspection outputs with existing PLC/SCADA systems without requiring a rip-and-replace of established draw-tower equipment.
Analytics and full traceability
A centralized dashboard surfaces defect trends by spool, line, and shift, with every event timestamped and mapped to its exact position on the fiber.
Five production-line problems, five targeted fixes.
High line speed too fast for conventional inspection
Conventional inspection approaches could not keep pace with fiber moving through the draw process at production speed.
High-speed imaging and edge inference
We deployed high-speed line-scan imaging paired with edge-optimized inference models capable of processing frames at full draw speed without introducing latency.
Distinguishing genuine defects from optical noise
Imaging at production speed can produce optical artifacts that must be separated from actual fiber-quality defects.
Production-trained defect classification
We trained classification models on labeled production imagery and tuned confidence thresholds to minimize false positives while preserving sensitivity to real defects.
Integrating with existing draw-tower control systems
Inspection outputs needed to work with existing PLC/SCADA infrastructure without replacing established production equipment.
Dedicated industrial-IoT integration layer
We connected inspection outputs with legacy PLC/SCADA systems through a dedicated integration layer without requiring a rip-and-replace approach.
Delivering real-time feedback without disrupting the draw process
Inspection and alerting could not become a production bottleneck or interrupt the continuous drawing operation.
Asynchronous edge-first processing
An asynchronous processing pipeline runs inspection and alerting in parallel with the draw process rather than gating production on inspection completion.
Maintaining traceability across continuous production
High-volume continuous fiber production generated inspection events that needed to remain tied to exact spool and line positions.
Timestamped spool-level event logging
Centralized data logging ties every inspection event to unique spool and line IDs and the exact fiber-length position for complete traceability.
"The inspection architecture had to operate at production speed without introducing latency into the fiber draw process. An edge-first pipeline allowed computer vision, defect classification, alerting, and control integration to operate alongside production rather than becoming a bottleneck."
From offline sampling to continuous quality intelligence at the line.
Defects are caught at the point of origin instead of during offline testing, reducing downstream failures and providing quality engineers with an auditable record of every spool.
Instant, actionable inspection alerts help production and line operators reduce scrap and improve first-pass yield without slowing the draw process.
Full defect traceability and trend analytics support root-cause analysis, warranty-risk reduction, and objective supplier and customer quality discussions.
The modular architecture lowers cost of quality and can be replicated across multiple draw towers and plants, creating a meaningful quality differentiator for telecom and hyperscale cable contracts.
Quality control embedded directly into the fiber draw process.
Optical Fiber Line Inspection moves quality control from an offline, sample-based checkpoint to a continuous, real-time capability embedded directly in the draw process itself. By integrating high-speed inline vision, edge AI inference, and closed-loop feedback into the fiber draw tower, the solution catches diameter variance, bubbles, and coating defects at the exact moment and location they occur — rather than after the fiber has already left the line. Its modular, edge-first architecture is designed to scale across multiple draw towers and plants, positioning the manufacturer for future extensions such as predictive maintenance and AI-driven yield optimization as a next-generation quality intelligence platform.
Common questions about this inspection deployment.
Find quick answers to common questions about inline optical fiber inspection, edge AI, PLC/SCADA integration, and production-line traceability.
Why is inline inspection necessary for optical fiber manufacturing?
Optical fiber is drawn at extremely high production speeds, so diameter variance, bubbles or voids, and coating inconsistencies can occur in fractions of a second. Offline sampling and end-of-spool testing may identify defects only after defective fiber has already been drawn or spooled. Inline inspection continuously monitors the fiber during production so defects can be identified at their point of origin.
How does the system inspect fiber at full production speed?
High-speed line-scan and area-scan cameras continuously image the fiber while edge AI inference processes inspection data locally at the production line. The edge-first architecture avoids cloud round-trip latency and allows inspection and alerting to operate at draw-line speed.
What types of optical fiber defects can the system detect?
The inspection system detects diameter variance, internal bubbles and voids, and coating-related defects including coating thickness inconsistency, eccentricity, and surface irregularities. Defects are classified so the production team can take targeted corrective action.
Can inspection results be connected to the draw-tower control system?
Yes. Inspection outputs integrate with existing PLC/SCADA systems through an industrial-IoT integration layer. The system can trigger real-time alarms and support automatic or operator-guided line adjustments without requiring replacement of existing production-line control equipment.
How is defect traceability maintained across continuous fiber production?
Every inspection event is timestamped and mapped to unique spool and line identifiers together with the exact fiber-length position. This creates a complete historical record that allows quality teams to trace a defect back to a precise location on a specific spool.
Still relying on offline sampling for production-line quality?
Our architects can map an inline computer-vision inspection architecture for your production line — cameras, edge AI, PLC/SCADA integration, alerts, and traceability — starting with a practical working pilot.
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