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

INVESTMENT CASTING PROCESS: 3D TEMPERATURE MAP RECONSTRUCTION OF A CERAMIC SHELL MOLD

The purpose of this work is to experimentally reconstruct the 3D temperature map of a ceramic shell mold and to investigate its time-dependent thermal behavior during the final part of the investment casting process. The investment casting process is the most used technique to produce very reliable components. Nowadays, there are still too much failing processes that are unacceptable in terms of both plant downtime and material waste. These failing processes are mainly caused by a non-uniform cooling that can promote thermal gradients in the components leading to a generation of residual thermal stresses. The experimental 3D temperature map reconstruction and its time-dependent behavior are analyzed through the Infrared Thermography technique. Such a reconstruction allows to observe the presence of strong temperature gradients and quantify them. In the present case, the central part of the investigated ceramic shell mold shows strong temperature gradient. This central part is of extreme importance because of the presence of the blades housings. The temperature difference present on the blades housings increases with time leading to stronger temperature gradient