Plating and Coating Inspection AI

Add a real-time inspection layer to every rack, barrel, and reel with vision AI for plating and coating inspection in metal finishing.

Plating and Finish Defects:

• Detect burning, dullness, and roughness from high current density at edges and corners, and dull, low-coverage areas in recesses and low current density zones, on racked and barrel-plated parts
• Detect pitting, nodules, roughness, and blistering from bath contamination, poor cleaning, or hydrogen, and peeling and poor adhesion at bends and edges
• Detect skip plating, bare spots, and voids on reel-to-reel strip, selective plating, and galvanized parts, so a skip is caught in feet rather than hundreds of feet

Appearance, Color, and Coverage:

• Verify color and appearance on anodized, chromated, passivated, and decorative finishes against the master, and catch streaks, water marks, rinse stains, and rack marks before they ship
• Detect uncoated and thin areas on hot-dip galvanized parts, including under hang points and in fastener holes, and flag dross, ash, and drips
• Verify masking and selective plating boundaries, so plating is where it should be and not where it should not, on connectors, contacts, and masked threads

Line Control, Traceability, and Systems Integration:

• Attribute each defect to the rack position, barrel load, bath, rectifier setting, and lot, so a rising burn rate on rack position 4 or a pitting trend after a bath addition is found from the data
• Route reject, strip-and-replate, and rework decisions in real time, so a burned rack is pulled before the next one is loaded the same way
• Push results, images, and lot records into MES, quality, and line control systems through PLC and API integration, alongside the XRF and thickness data the shop already logs

More About Plating and Coating Inspection

What is plating inspection with Vision AI?

Plating and coating inspection with vision AI uses cameras at the unrack, unload, exit, and packing stations of finishing lines and deep-learning models to inspect every plated, anodized, galvanized, or coated part for the defects a thickness gauge and an XRF sample cannot see: burning and dullness from current density, pitting, nodules, blistering, and roughness, peeling and adhesion failures, skip plating and bare spots, color and appearance deviation, streaks and stains, rack and contact marks, and masking errors. Each part or strip segment gets a pass, a reject, or a rework routing, findings are attributed to the rack, barrel, bath, and lot for root cause, and results feed MES, quality, and line control alongside existing thickness and composition data. It sits next to the coatings pages for powder coating inspection and conformal coating inspection, which cover organic and PCB coatings rather than electrochemical and hot-dip finishes.

Can it integrate with our line controls, MES, and quality systems?

Yes. Roboflow Inference (opens in a new tab) runs on an edge device at the line and exposes a standard API and common industrial protocols, so inspection results, images, and lot records flow into your existing systems: hoist and line controls and rectifier controllers, PLCs from Allen-Bradley and Siemens for reject and rework routing, MES and job tracking, quality systems and certificates of conformance, XRF and thickness measurement databases, bath chemistry and control systems, and customer portals, through REST, MQTT, OPC UA, discrete I/O, and direct database writes. PLC-level integration routes a burned rack to strip on the spot, and every result carries part, lot, rack or barrel, bath, rectifier setting, timestamp, defect class, location, and the image, with a full record behind every certificate and every customer question.

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