Ontology-Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence-Empowered Data Analytics

Abstract

One of the key challenges of data management for smart manufacturing is dealing with data originating from both physical processes and virtual digital technologies. As image and sensor-based production monitoring deliver a wealth of data along the process chain, artificial intelligence (AI) enables enhanced data analysis and new insight regarding relevance of observed process deviations. With constantly increased availability of data from manifold and specific sources, the complexity and heterogeneity of information structures are also growing rapidly. This is especially true for highly variable research, scale-up, and pilot production, which poses new demands on data acquisition, data management, and data preprocessing. Herein, a unified framework for integrating an ontology and graph-based data space with data acquisition and data analytics to improve data consistency, documentation of workflows, as well as the reproducibility of observations and results is presented. The framework consists of several open-source web services that form an ontology-based data space where physical and virtual process chains are represented by a semantic data fabric built from findable, accessible, interoperable, and reusable resource descriptions framework self-descriptions. The feasibility of the proposed framework is demonstrated for a laboratory-scale Li-ion battery cell production facility with AI applied to two data analytics use cases.