Expert System for the Machine Learning Pipeline in Manufacturing

Many success stories already exist with regard to the implementation of Machine Learning (ML) and Artificial Intelligence (AI) in manufacturing. However, companies with traditional focus on production technologies face challenges in conducting AI-projects successfully and lack knowledge of which obstacles may occur and how to decide in the implementation phase. In this paper, we develop an approach that focuses on the methodological necessary steps for the successful application of ML and AI in manufacturing. Optimization potentials and decisions to be made are outlined in every step. A main focus is put on optimizing hyperparameters of ML-models as one promising approach for improving overall ML-model performance. An expert system is presented that enables the selection of suitable hyperparameter optimization techniques. The concept is validated based on manufacturing of compressor components of a turbofan engine.