A leading global automotive supplier was relying on sample destructive testing to ensure quality standards were being met on a critical welding process. The random nature of this testing exposed them to significant recall risk. It engaged Eigen to help them see better welds.
Destructive Testing? Not Anymore.
Before – Destructive Testing
- Quality Engineers were manually destructing sample parts each shift to test weld thickness.
- Testing required 1 hour per shift plus the cost of the destructed product.
- Lagging quality metrics did not allow Process & Quality Engineers to detect or troubleshoot issues in-process.
- Disparate quality data resulted in multiple meetings/days to determine root cause when the OEM escalated an issue.
After – Eigen Quality and Process Monitoring
- FLIR thermal cameras captured various views of the weld process and were used to generate a virtual part image for part-to-part monitoring.
- Eigen Machine Learning specialists designed a monitoring algorithm that predicted weld thickness to replicate real-time quality testing for all tanks post welding process.
- Insights generated from correlated image and process data delivered through Eigen’s online platform streamlined troubleshooting and root cause investigations.
- Manufacturer adopting Eigen’s platform across NA operations and updating control plan with OEM to replace manual quality assessment processes with Eigen’s automated system.