A driver of falling productivity is the quality control used, done with traditional pass/fail sampling. We propose to research root-cause corrective quality control; to develop process physics models integrated with wireless process sensing for the factory floor. Such model-integrated quality-control is a breakthrough over current tolerance based statistical methods available, it will eliminate inefficient engineering quality-control tasks and enable much lower rates of rework and scrap.
A radical increase in quality control is necessary, making use of easily deployed process sensors and integrated with easily built modular physical simulation models to understand and mitigate deep root cause problems, to prevent root cause variations from impacting product performance.
This proposal is therefore focused on integration of
1. Process simulation
2. Wireless sensors
3. Data analytics
And integrating these technologies into a rapid root cause analysis system.