Support Vector Representation Machine for superalloy investment casting optimization
Abstract 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.
In this paper, the structure characteristics of a transmitting device with stepped fit for high speed motorized spindles, and a detailed process of stress analysis to the shrink fitted joint in finite element analysis method is introduced. The reasonability of the structure design and reliability of the surplus joint are verified by analysis results.