Ontology-based approach to enable information integration for digitalised manual work instructions

The dynamic market in the era of Industry 4.0, driven by individualised product needs, shorter lifecycles, and global competition, necessitates manufacturing companies to offer more customised and functionally complex products while simultaneously addressing the growing skilled worker shortage. This shortage is critical, especially in manual work processes, where adaptive work instructions are essential to assure productivity and accuracy. Despite the emphasis on digitalisation in manufacturing, most companies have not fully digitalised their work instructions. Although many instructions are in digital formats, such as PDFs, they are designed for paper-based usage and remain static. Companies lack the ability to adapt to order changes or evolving production needs, which hinders their ability to respond to market shifts. This dissertation identifies the limitations of current work instruction systems and proposes a semantic data model that integrates all contextual information. The goal is to achieve holistic information integration and complete digitalisation of manual work instructions. Ontological modelling for digital twins, which dynamically mirror physical objects, forms the foundation of the proposed approach, allowing real-time updates and adaptive modifications of work instructions. This dissertation also presents a systematic solution for data extraction from heterogeneous sources, enabling the structured instantiation of the ontology and information integration for enhanced decision support. Extensive case studies validate the proposed approach, demonstrating improved adaptability and applicability of digitalised manual work instructions. Ultimately, the dissertation suggests an ontology-based approach to enhance the flexibility and context-specificity of manual work instructions, furthering digital transformation in manufacturing. The approach thereby advances the practical implementation of digitalisation in modern manufacturing environments and forms a promising theoretical foundation for further research on digitalised manual work instruction.