Abstract
Machine learning (ML) based data analysis has attracted an increasing attention in the manufacturing industry, however, many challenges hamper their wide spread adoption. The main challenges are the high costs of labour-intensive data preparation from diverse sources and processes, the asymmetrical backgrounds of the experts involved in manufacturing analyses that impede efficient communication between them, and the lack of generalisability of ML models tailored to specific applications. Our semantically enhanced ML pipeline, SemFE, with feature engineering addresses these challenges, serving as a bridge to bring the endeavours of experts together, and making data science accessible to non-ML-experts. SemFE relies on ontologies for discrete manufacturing monitoring that encapsulate domain and ML knowledge; it has five novel semantic modules for automation of ML-pipeline development and user-friendly GUIs. The demo attendees will be able to use our system to build manufacturing monitoring ML pipelines, and to design their own pipelines with minimal prior knowledge of machine learning.