How MordeTech replaced manual visual QC with a real-time deep learning inspection system on a high-speed metal stamping line — achieving 94% reduction in defect escapes within 90 days.
The client's stamping line ran at 800 parts per minute producing structural body components. Manual visual inspection at this speed was impossible — operators were catching fewer than 40% of surface defects, micro-cracks, and dimensional deviations before parts reached downstream assembly.
The existing inline gauging system only checked a small sample (1-in-50) and couldn't detect surface anomalies. Warranty claims from assembly partners were rising, and an IATF 16949 audit had flagged the QC process as a high-risk non-conformance. The client needed a 100% inline inspection solution with zero impact on line speed.
MordeTech conducted a 3-day site assessment to map defect types, lighting conditions, and PLC integration requirements. We identified four primary defect categories to target:
We designed a custom inspection cell with four Basler acA4024 cameras (12.3MP) arranged around a 360° coverage fixture, triggered by the existing PLC conveyor encoder. An NVIDIA Jetson AGX Orin edge AI module runs the inference on-device, ensuring sub-15ms latency well within the part's 75ms conveyor window.
The deep learning model was trained on 28,000 labelled images captured from the line over a 2-week baseline period, augmented with synthetic defect generation. Training used a custom ResNet-based architecture fine-tuned with transfer learning from an automotive-specific defect dataset.
"We had tried off-the-shelf vision systems before — they couldn't handle the reflective surfaces and speed. MordeTech built something completely custom that just works. The ROI was visible within the first month."
— Quality Manager, Tier-1 Automotive Supplier, Pune (name withheld at client request)See how MordeTech has delivered measurable results across manufacturing, energy, and process industries.