Abstract
• Original and innovative application of Machine Learning techniques to investment casting industry. • Key performance indicators to simultaneously increase quality and reduce defects in final products (i.e. turbine blades). • Innovative solutions of Support Vector Representation Machine technique to define set-points of Key performance indicators. Machine learning techniques have been widely applied to production processes with the aim of improving product quality, supporting decision-making, or implementing process diagnostics. These techniques proved particularly useful in the investment casting manufacturing industry, where huge variety of heterogeneous data, related to different production processes, can be gathered and recorded but where traditional models fail due to the complexity of the production process. In this study, we apply Support Vector Representation Machine to production data from a manufacturing plant producing turbine blades through investment casting. We obtain an instance ranking that may be used to infer proper values of process parameter set-points.
Highlights
Turbine blades are critical components in aeronautics and gas industry
We studied and analyzed production data from Europea Microfusioni Aerospaziali (EMA), an investment casting foundry placed in Southern Italy producing turbine blades for jet-engines in civil and defence aerospace, marine and energy industry
Process data, that is all parameters detected during the production process. This set is composed of strictly technological variables, directly related to the materials used in the various stages, or indirectly referring to the settings of the plants; These two sets of data have been the source from which we identified the relationship between process parameters and product attributes
Summary
Turbine blades are critical components in aeronautics and gas industry. Blades operate under extreme and complex environmental conditions; they are subjected to air dynamics and centrifugal forces causing tensile and bending stresses: for example, in$Partially supported by PON03PE-00111-01 "MATEMI" financed by the Italian Ministry for University and ResearchPreprint submitted to Applied Mathematical Modelling aeroplane engines, turbines often reach 1050◦C for several thousand hours [1, 2]. Turbine blades are critical components in aeronautics and gas industry. Blades operate under extreme and complex environmental conditions; they are subjected to air dynamics and centrifugal forces causing tensile and bending stresses: for example, in. Turbine performance significantly depends on the shape and dimensions of blades as well as their material strength. To warrant resistance against intense mechanical loads, the manufacturing process must satisfy high quality requirements. Blades must be produced respecting stringent dimensional and geometrical tolerances as well as ensuring material integrity and mechanical properties (e.g. high temperature tensile strength, endurance strength and creep strength). Blades are manufactured through the investment casting or “lost-wax” process often employed in the production of high quality and net-shaped complex parts [3, 4]
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