A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects
Foundry process is a complex process with more than 100 parameters that influence the quality of final cast component. It is a process with multiple optimal conditions. For two foundries manufacturing the same alloy and cast geometry, the process and alloy conditions used by one foundry will most likely be different from the other one. For a foundry process engineer, it is also currently difficult to link process knowledge available in the published literature to specific process conditions and defects in a foundry. A concept of product and foundry specific process knowledge has been introduced so that the intellectual property that is created every time a cast component is poured can be stored and reused in order to be able to reduce defects. A methodology has been proposed for discovering noise free correlations and interactions in the data collected during a stable casting process so that small adjustments can be made to several process factors in order to progress towards the zero defects manufacturing environment. The concepts have been demonstrated on actual but anonymised in-process data set on chemical composition for a nickel based alloy. (C) 2013 Elsevier B.V. All rights reserved.
casting process optimization machine learning quality control six sigma 7 epsilon