Detect
Define the visible condition, its minimum relevant size and the part of the web that must be observed.
Resources / AI Machine Vision Quality Control
AI Machine Vision for Roll-to-Roll Quality Control
Machine vision can make defects visible and create a repeatable inspection record. On a moving web, useful results still depend on a defined defect, stable imaging conditions, known web position and a planned response when the system finds an exception.
Inspection planning
Make the inspection target and response path explicit
A useful inspection project connects the imaging setup to the moving web, the defect definition and the production action that follows a confirmed result.
What machine vision can inspect on a moving web
In a roll-to-roll inspection implementation, optical imaging can be paired with position-aware records so that an observed feature can be located along and across the web. Fraunhofer FEP describes this approach for its own roll-to-roll inspection system, where line-scan inspection produces a roll map and defects are classified by brightness and shape.[1]
The inspection target must be specific. A project may look for coating-edge variation, contamination, print variation, wrinkles or another defined feature; the camera, lighting, resolution and acceptance rule should be selected for that target rather than for a generic claim of AI quality control.
Locate
Decide whether the result needs a machine-direction position, cross-web position, roll map or only a pass/fail event.[1]
Respond
Specify the operator alert, marking, reject, slowdown or investigation path before commissioning the inspection system.
Prepare the imaging conditions before training or tuning
A 2024 roll-to-roll slot-die coating study identifies image quality and resolution, colour variability, lighting conditions, coating-material characteristics, environmental factors and algorithm parameters as factors that can influence its edge-defect method. Treat these as a commissioning checklist for a comparable vision task, not as a universal performance specification.[2]
- Collect representative images from normal production, including expected material variation and the operating states in which faults are reported.
- Set and document the optical arrangement: field of view, image resolution, lighting geometry, exposure behaviour and cleaning access.[2]
- Agree the acceptance rule with quality and production teams, including how uncertain cases are reviewed and recorded.
- Recheck the result after material, surface, lighting, speed or process changes rather than assuming a configuration transfers unchanged.[2]
Connect inspection to the actual web path
The camera observes the web presented to it; it does not physically hold the web on a reference path. KRD engineering guidance is to map the inspection point, the material span, web-guide location, tension zone, speed range and downstream operation so that observed variation can be separated from an imaging or handling condition.
For roll-to-roll transport, published experimental work treats longitudinal and lateral web dynamics as separate control concerns and uses tension and velocity control in the tested system. That does not prescribe a single control architecture, but it supports recording relevant handling conditions during inspection trials.[3]
Web position
Record the selected edge, centreline or printed reference and confirm that it remains within the intended camera view.
Tension and speed
Record the process values and operating state associated with confirmed defects so the review can distinguish production conditions from image evidence.[3]
Process response
Relate a confirmed inspection event to a defined review or machine action; do not treat a classification result as an automatic root-cause diagnosis.
Where AI fits in the inspection project
PMMI reported in March 2026 that, among the AI applications discussed in its packaging-equipment report, knowledge transfer and machine vision had the highest momentum. The same report identifies operational readiness, existing data infrastructure, ROI, latency and accountability among the implementation considerations. This is packaging-industry context, not evidence that an AI model is required for every web-inspection task.[4]
Start with the inspection outcome and evidence needed to make a production decision. Use conventional image processing, a model-based method or a combination only after the target appearance, available examples, review route and operating constraints are understood.
How KRD supports inspection-ready web handling
KRD Automation can help scope the web-guiding, tension-control and sensor considerations around an inspection location. The goal is to clarify the material path, reference feature, available mounting space and process signals before a machine-level configuration is evaluated.
Share a web-path drawing, material details, speed range, target defect, inspection location and the action expected after a confirmed event. Final integration and acceptance criteria should be verified on the actual machine.
Sources & References
- Fraunhofer Institute for Electron Beam and Plasma Technology FEP. Roll-to-roll inspection system Back
- Polymers. Image Data-Centric Visual Feature Selection on Roll-to-Roll Slot-Die Coating Systems for Edge Wave Coating Defect Detection Back
- Sensors. Experimental Validation of High Precision Web Handling for a Two-Actuator-Based Roll-to-Roll System Back
- PMMI, The Association for Packaging and Processing Technologies. AI Gains Ground in Packaging Industry Back
FAQ
Can AI machine vision replace web guiding?
No. Inspection observes the web presented to the camera; a web-guiding system is a separate physical control function that maintains a selected lateral reference on the machine.
What should be decided before selecting an inspection camera?
Define the defect, minimum relevant feature, material appearance, inspection location, speed range, field of view, lighting, position record and the production action that follows a confirmed event.
Should every roll-to-roll inspection project use AI?
Not automatically. Start with the inspection outcome and evidence needed for a decision, then choose the image-processing or model-based approach that can be validated for the material and operating conditions.
Plan an inspection-ready web path
Share the material, speed range, target defect, inspection position and web-handling challenge. KRD can help identify the guiding, tension and sensing considerations to evaluate around the inspection point.