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.

► Innovative approach for casting process optimization using in-process data.
► Ability to discover foundry and product specific process knowledge.
► Ability to associate casting processing conditions with product specifications.
► Ability to reduce rejection rates by 1% from existing 5% level.
► A toolkit that would be of immediate use to casting process engineers across the world.
► A methodology that is transferable to other net shape forming processes and beyond.