Computer Vision in Quality Control: How AI Is Revolutionizing Visual Inspection
When a defect slips through, costs explode: rework, scrap, customer complaints. Computer Vision with Artificial Intelligence changes the game: “smart” cameras check every part in seconds, always with the same criteria.
The real difference comes from the integration with our MES, SkyMes—the “orchestra conductor” that coordinates orders, resources, and quality, turning vision data into immediate, actionable decisions.
What actually happens with SkyMes
In-line checks: the camera shoots, AI evaluates, and SkyMes receives the result (OK / Needs review) in real time.
No bottlenecks: good parts keep flowing; doubtful ones are automatically diverted.
Clear traceability for every piece: SkyMes links result, shift, operator, lot—and saves the image when useful.
Smart alerts: if defects rise, SkyMes notifies the right people and suggests actions (cleaning, tool change, setup check).
Why connect vision to SkyMes
Guided workflows: for “NOK” parts, SkyMes automatically opens a rework task with clear instructions and full traceability to closure.
Simple, useful reports: dashboards for first-pass-yield, scrap by cause, trends by shift/supplier/line.
End-to-end traceability: from production order down to the serial number—great for audits and customer responses.
Scalability: start with one station, then extend to other lines without reinventing the process.
Smooth integration: SkyMes talks to PLCs and existing systems; vision becomes a quality module, not a bolt-on.
What AI can see (and how SkyMes makes it useful)
Surface defects: scratches, stains, dents → SkyMes records defect type and monitors frequency.
Missing or mis-assembled parts → automatic ticket creation and part hold/diversion.
Code reading (barcode/Datamatrix) → traceability always in sync.
Critical dimensions and alignment → SkyMes flags out-of-tolerance items and highlights trends before scrap spikes.
What changes for people on the shop floor
Operators: fewer repetitive checks, more focus on flagged cases with images/examples.
Quality teams: clear view of recurring causes and the impact of corrective actions.
Production: faster, data-driven decisions—not gut feeling.
Management: straightforward KPIs (scrap, FPY, avoided costs) directly in SkyMes.
How to introduce it without stopping production (the SkyMes path)
Pick a high-impact use case (e.g., glossy finishes with frequent defects).
Run a quick pilot on one station: measure benefits in a few weeks.
Tune lighting and fixturing for reliable images.
Connect to SkyMes: clear OK/NOK rules, automatic diversion, evidence storage.
Lightweight training: visual examples of “good/not good” and how to use the reports.
Scale gradually to other product families or lines.
Expected results (plain and simple)
Less scrap and rework.
More first-pass OK parts.
Shorter inspection times and smoother lines.
Faster reactions when problems arise.
ROI often within months thanks to avoided costs—visible end-to-end in SkyMes.
Questions we hear a lot
Do we need a huge defect library to start?
No. Often you begin by teaching AI what a good part is. The rest improves over time, with SkyMes storing useful examples.
Cloud or on-prem?
For fast cycles, keep analysis close to the line; SkyMes manages data and flows, while cloud is great for history and model improvements.
What do we store, and for how long?
With SkyMes you set a simple policy: always keep images for defectives, sample the goods; define clear retention times.
In short
Computer Vision with AI, integrated into SkyMes, turns quality control from a manual, costly task into an automatic, consistent, and measurable process. Fewer customer issues, less waste, and calmer, more effective teams.