Cognitive Decision-making Systems for Scraps Control in Aerospace Turbine Blade Casting

Abstract

The competitiveness of a casting system in modern lost wax production of superalloy turbine blades strongly depends on the reduction of scraps, which commonly affect superalloy cast parts. In order to achieve a focused goal of competitiveness, some key and vital parameters (Key Process Variables) have to be continuously taken under control to make very accurate predictions of Target Variables, which represent, as mapped KPVs domain, the ultimate performance of the entire production link.Such an approach is based on the development of robust control monitoring of the ceramic shell manufacture, which is specifically conceived to foster a possible reduction of scraps in the production if superalloy components. The concerned control will take into consideration data coming from both sensors and measured values in laboratory. The sensor data, which is originated from both new adopted inline and offline equipments at Europea Microfusioni Aerospaziali S.p.A. (EMA) and data measured in the EMA laboratories, will be merged into a sensor pattern vector which represents the basis to develop the EMA demonstrator within the Intelligent Fault Correction and self Optimizing manufacturing systems EU project funded in FP7. The sensor pattern vector will be used to feed an automatic system for the prediction of the process vital parameters. An automated system, based on artificial intelligence paradigms, in particular neural networks, will be fed with the data coming from the sensor pattern vector in order to produce an optimal multi-object output.