  During the fabrication of casting parts sensor data is typically
automatically recorded and accumulated for process monitoring and defect
diagnosis. As casting is a thermal process with many interacting process
parameters, root cause analysis tends to be tedious and ineffective. We show
how a decomposition based on non-negative matrix factorization (NMF), which is
guided by a knowledge-based initialization strategy, is able to extract
physical meaningful sources from temperature time series collected during a
thermal manufacturing process. The approach assumes the time series to be
generated by a superposition of several simultaneously acting component
processes. NMF is able to reverse the superposition and to identify the hidden
component processes. The latter can be linked to ongoing physical phenomena and
process variables, which cannot be monitored directly. Our approach provides
new insights into the underlying physics and offers a tool, which can assist in
diagnosing defect causes. We demonstrate our method by applying it to real
world data, collected in a foundry during the series production of casting
parts for the automobile industry.
