Brazing Joint Inspection AI

Add a real-time inspection layer to every brazed joint with vision AI for brazing joint inspection. Built for the lines where a fillet that looks closed from the operator's side is open on the back of the bend, a joint that ran hot leaves the station looking finished once the soot wipes off, and the first hard evidence anything went wrong arrives at pressure decay with a completed coil already built around it. Whether you're brazing coil returns and headers, refrigeration tube and hose assemblies, or surgical instrument and hypotube joints, Roboflow inspects every joint at the station on the cameras your line already runs, with a per-joint record behind every acceptance.

Fillet, Fill, and Joint Formation:

• Detect incomplete fillets, open joints, and the partial wetting that leaves a leak path even when the front side looks finished
• Flag insufficient filler and excess flooding, and the runs that show a joint took far more filler than the procedure called for
• Verify insertion depth and tube-to-fitting alignment before heat, where a joint seated short can never be recovered afterward

Heat, Oxidation, and Filler Behavior:

• Catch overheated joints, oxidation, and burn signatures that mean the filler never wetted the way the procedure assumed
• Detect underheated joints where the filler balled instead of flowing, along with voids, porosity, and blowholes at the fillet
• Flag flux residue and contamination that hides a defect from the operator's own visual check at the station

Traceability, Leak-Test Routing, and Records:

• Trend defects by operator, station, torch, filler lot, and joint type, so the fix lands on the cause rather than on one joint
• Route suspect assemblies to rework before the coil is built out and before pressure decay finds it the expensive way
• Keep a per-joint image record that supports brazing procedure qualification, customer PPAP or device history records, and warranty investigation

Bring intelligence to every joint today. Stop an open fillet from becoming a failed pressure test, a warranty leak, or refrigerant on the floor.

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More About Brazing Joint Inspection

What is brazing joint inspection with Vision AI?

Brazing joint inspection with vision AI uses computer vision models to inspect brazed joints at or just after the brazing station, catching the conditions that decide whether a joint holds pressure: incomplete or open fillets, insufficient and excess filler, partial wetting, oxidation and overheat signatures, voids and blowholes at the fillet, flux residue, and tube insertion depth before heat is applied. This is a different process from soldering, which happens below 450 degrees Celsius and is covered on our solder joint inspection page for electronics; brazing uses filler metals above that threshold on copper, brass, and steel assemblies. Models trained on your actual joint geometries and filler metals apply your acceptance criteria at line rate and produce a per-joint record that supports AWS B2.2 procedure qualification, ASME Section IX Part QB, and ISO 9001 or ISO 13485 quality programs.

Can Vision AI find voids inside the joint, or only surface defects?

Only what the camera can see, and that boundary is worth being exact about because it determines where this fits in a quality plan. A camera inspects the external fillet and the surface condition around it: fillet continuity and coverage, wetting behavior, filler volume, oxidation and heat signature, blowholes that break the surface, and joint alignment. It cannot see a void enclosed inside the lap. Internal porosity remains the domain of X-ray or CT, and ultrasonic where geometry allows. What makes vision worth deploying anyway is that external fillet formation and heat signature are strong indicators of the process conditions that create internal voids, so a station that starts producing poorly wetted, oxidized, or filler-starved fillets is the same station about to produce internal defects. Catching that drift at the station, on every joint rather than on an X-ray sample, is where the return lives, and it complements sampling NDT rather than replacing it.

Does brazing joint inspection support AWS B2.2 and ASME Section IX qualification?

Yes. Roboflow inspection can run as documented in-process verification supporting AWS B2.2 and B2.2M brazing procedure and performance qualification, AWS C3.4 for torch brazing, ASME Section IX Part QB, ISO 13585 for brazer qualification, and ISO 9001 or ISO 13485 quality management systems, generating the per-joint imagery, defect classification, and operator and station trending that qualification records and audits run on. Roboflow is the inspection engine; your welding and quality engineering teams own the brazing procedure specification, the acceptance criteria, the destructive and NDT testing regimen, and brazer qualification. Vision inspection supplements a qualified brazing program rather than substituting for the testing those codes require.

Can it integrate with our MES, traceability, and leak-test station?

Yes. Joint-level results, defect classes, and pass and fail states push into MES and quality systems like SAP, Ignition, Wonderware, and AVEVA through REST, MQTT, OPC UA, and direct database writes, so a flagged assembly carries its record forward instead of being discovered downstream. Results can tie to the coil or assembly serial read by the same camera or a barcode station, so a joint trends against the operator, torch, and filler lot behind it. At the line level, pass and fail can drive PLC logic to divert a suspect assembly to rework before it reaches pressure decay, and imagery can write to a historian for PPAP packages, device history records, and warranty investigations.

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