Ceramic Defect Detection AI

Add a real-time inspection layer to every piece with vision AI for ceramic defect detection. Built for the operations where the surface is glossy and specular, the product carries a printed pattern or a natural variation that a defect has to be told apart from, the crack that fails the piece is a hairline in the glaze that shows at one angle, and the sorter has a second or two per tile. Roboflow inspects the surface, the glaze, the edge, and the print on every piece, with a graded per-piece record behind every carton.

Body and Glaze:

• Detect cracks, hairlines, chips, and body defects on fired pieces, including the hairline crack in the glaze that only shows under raking or reflected light
• Detect glaze pinholes, blisters, crawl, crazing, orange peel, runs, and bare spots, so a glaze application or firing problem surfaces as a flagged piece and a named kiln zone
• Detect black spots, iron spots, specks, and contamination in the body and the glaze, and tell them from the pattern they sit on

Print, Pattern, and Shade:

• Detect decoration and digital print defects, misregistration, banding, missing ink, and nozzle dropouts on printed tile and tableware, so a printhead fault is a work order instead of a pallet
• Separate defects from design and natural variation on patterned, marbled, and textured product, so a vein in the print is not a rejected tile and a crack across it is
• Grade shade and tone against the reference, so a carton holds one shade and the sorter's grade is a record

Dimensions, Edge, and Systems Integration:

• Detect edge chips, corner breaks, lippage-related damage, and glaze faults at the edge, so the piece that will not lay flat or finish clean is caught at the sorter
• Trend defects by kiln, kiln zone, glaze line, printhead, and shift, so the drift surfaces before it becomes a run of second grade
• Integrate with sorting machines, MES, and quality systems, with per-piece records tied to lot, kiln, grade, and imagery for claims and audits

Bring intelligence to every piece today. Stop hairline cracks, glaze pinholes, and print faults from becoming second-grade cartons, water-test failures, or the lot sent back from the job site.

More About Ceramic Defect Detection

What is ceramic defect detection with Vision AI?

Ceramic defect detection with vision AI uses computer vision models to inspect fired and glazed ceramic pieces at the kiln exit, on glazing and decoration lines, and at sorting and packing: cracks, hairlines, and chips, glaze pinholes, blisters, crawl, crazing, orange peel, and runs, black spots and contamination, print and decoration misregistration and dropouts, edge and corner damage, and shade against the reference, on tile, sanitaryware, tableware, and technical ceramics. Models trained on your actual product, glazes, patterns, and lighting run at line speed on every piece, with per-piece grade records that support ISO 13006 and ANSI A137.1, ISO 10545, ASME A112.19.2, and ISO 9001.

Can Vision AI tell a hairline crack from the pattern on a printed tile?

Telling a defect from the design is one of the hardest calls in ceramics inspection: the surface is glossy and reflects the lights, the tile carries a printed marble vein or a textured pattern that varies from piece to piece by design, a hairline crack in the glaze shows only under raking or reflected light, and the sorter gets one look at each tile. Deep-learning models trained on your actual product, patterns, glazes, and lighting learn what a crack, a pinhole, a black spot, and a print dropout look like on your tiles and what your patterns look like when they are right, and multi-angle imaging with diffuse, raking, and reflected illumination gives the model the views an inspector would tilt the tile to get. Anomaly detection trained on good pieces catches the defect you have not labeled yet on a new pattern, and every flagged piece comes back with the location and the frame, so the grade is a record and not a judgment call.

Does this replace our caliber and planarity gauges?

No. Caliber, planarity, and warpage gauges keep their role measuring the dimensions ISO 13006 and ANSI A137.1 require, and the sorting machine keeps its size and grade logic and its stacking. Vision AI adds what a dimensional gauge does not see: the surface and the glaze and their defects, cracks and chips, black spots and contamination, print and decoration faults, edge damage, and shade, plus a picture of every flagged piece. Gauge readings and vision detections land in the same per-piece record and drive the same sorter, and the tile that would have measured in tolerance and shipped with a crack across the vein is graded before it is boxed.

Can it integrate with our sorting machines, MES, and quality systems?

Yes. Roboflow Inference exposes a standard API and supports common industrial protocols, so inspection results flow into your existing systems: sorting and stacking machines and their PLCs, MES and ERP platforms like SAP and Oracle, quality and SPC systems, and SCADA and HMI platforms like Ignition and AVEVA, through REST, MQTT, OPC UA, and direct database writes. PLC-level integration sends the grade to the sorter and diverts a rejected piece the moment a check fails, and every event carries the piece, lot, kiln, line, defect class, location, grade, imagery, and disposition, with a full record behind every carton for claims, audits, and process improvement.

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