• 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.