Stop Guessing Weld Quality in Robotic Cells
HeatCore AI™ replaces manual checks with inline thermography and defect classification. Know when a weld drifts, prove compliance, and keep uptime high.
Thermal defects slip past spot checks and trigger expensive rework
Robotic weld lines move fast. Operators rarely see porosity, overheated beads, or equipment drift until parts fail final inspection.
Where things go wrong
- No continuous visibility of weld pool temperature
- Defects only found after grinding, NDT, or customer complaints
- Manual reports slow audits and root-cause analysis
The cost
- Scrap and rework eat into margins
- Robots down while teams hunt for the last good part
- Auditors chase evidence, projects miss ISO 17635 commitments
Real-time thermography with defect AI
HeatCore AI™ bolts onto robotic cells, captures the weld pool at 60 FPS, and applies proprietary deep learning so you know which welds pass, which fail, and why.
Inline thermal visibility
640 × 512 microbolometer arrays with 12 µm pixel pitch track weld pool temperature and gradients in real time.
- Range: –40 °C to +2000 °C
- Sensitivity: < 50 mK NETD
- Arc-compensated, IP67 housing
Defect AI built for welding
Deep learning models and statistical pattern recognition trained on real weld data spot porosity, lack of fusion, and overheating instantly.
- 99.2% accuracy, < 100 ms decision time
- Edge computing—no cloud dependency
- Adapts to your materials and travel speeds
Ready for MES / QMS
Feeds alarms, SPC data, and weld evidence into Therness QMS Copilot™ or your existing systems.
- Modbus RTU/TCP, Ethernet/IP, OPC-UA
- REST API with OpenAPI 3.0 schema
- Pre-built PLC function blocks and digital I/O
Quality teams finally get proof, and operations keep moving
Compliance and reporting
- Automatic evidence pack per weld pass
- Export to ISO 17635 / ISO 3834 audits in seconds
- Feeds CAPA and PPAP inside QMS Copilot™
Operations and ROI
- Catch defects before cutting, machining, or painting
- Fast root-cause analysis with temperature timelines
- Robots stay productive with zero extra cycle time
HeatCore AI™ catching defects in real time
Watch how inline thermography and AI keep robotic weld cells compliant and productive.
Frequently asked questions
Quick answers for engineers and quality teams evaluating HeatCore AI: Thermal Weld Monitoring System | Therness.
What defects can HeatCore AI detect?
HeatCore AI flags thermal/anomaly patterns correlated with porosity risk, lack of fusion risk, overheating, and process drift. Coverage depends on material, joint geometry, and acceptance criteria configured for your process.
How fast can HeatCore AI be installed on a robotic cell?
Most deployments start with a pilot cell: mount the IR sensor, align optics, calibrate emissivity/thresholds, and validate against your WPS and acceptance criteria. Timeline depends on access to the cell and the number of joint types.
Does HeatCore AI integrate with MES or QMS tools?
Yes. You can export evidence packs (PDFs) and connect alarms/metrics via common industrial interfaces (e.g., OPC-UA/REST). It also integrates with Therness QMS Copilot to automate CAPA, PPAP, and audit evidence filing.
Which standards does HeatCore AI support for documentation?
HeatCore AI is designed to support ISO 17635 / ISO 3834 evidence workflows. Final compliance requirements depend on your applicable standard, customer requirements, and inspection plan.