Monitoring global and local variations in multichannel functional data for manufacturing processes

This study models global and local variations hidden in multichannel functional data (MFD) for the purpose of manufacturing process monitoring. With advances in sensing technology, online measurement of manufacturing process variables could take the shape of multichannel curves. Although MFD contains rich information about process conditions, it is a challenging issue to model and interpret complex variations in MFD for process change detection and process faulty condition discrimination. A new approach was developed in this paper to decompose each channel of functional data into global and local variation components. Based on the extracted patterns, a principal curve regression method was applied to detect and discriminate different process conditions. The method was validated by real data from a forging plant. A simulation study was also conducted to verify the approach for MFD with complex patterns.