Integrating information technology (IT) and operational technology (OT) in the manufacturing ecosystem is crucial for improving productivity, efficiency, and situational awareness. However, integrating various IT/OT systems is often time-consuming and expensive. Semantic integration resolves this by unifying data from heterogeneous sources while preserving the contextual meaning of each source. The success of semantic integration requires robust ontologies that describe objects, processes, and relationships for knowledge representation, system integration, and semantic interoperability. Despite the significance of ontologies in many industrial domains, a scientifically defined ontology for the motor manufacturing process is in demand. This research addressed this gap by applying the top-down approach with 5M (manpower, machine, method, measurement, and material) methodology to develop a generic motor winding process ontology systematically. Encoded in the Terse RDF Triple Language (TTL), the developed ontology systematically addressed the needs of diverse job roles by incorporating fundamental aspects such as motor types, winding techniques, and thermal classes. The ontology consisted of a clear definition of core classes and their relationships, and outlined the major factors influencing the motor winding process. Finally, validation experiments confirmed the robustness of the ontology through syntax validation, logical validation using “HermiT” reasoning, domain compatibility assessments via competency questions, and SPARQL query execution outputs. The results confirmed the robustness of the ontology and its applicability, offering a framework for semantic interoperability and knowledge representation in the motor winding process.

Industry 4.0 is helping to unleash a new age of digitalization across industries, leading to a data-driven, interoperable, and decentralized production process. To achieve this major transformation, one of the main requirements is to achieve interoperability across various systems and multiple devices. Ontologies have been used in numerous industrial projects to tackle the interoperability challenge in digital manufacturing. However, there is currently no semantic model in the literature that can be used to represent the industrial production workflow comprehensively while also integrating digitalized information from a variety of systems and contexts. To fill this gap, this paper proposed industrial production workflow ontologies (InPro) for formalizing and integrating production process information. We implemented the 5 M model (manpower, machine, material, method, and measurement) for InPro partitioning and module extraction. The InPro comprises seven main domain ontology modules including Entities, Agents, Machines, Materials, Methods, Measurements, and Production Processes. The Machines ontology module was developed leveraging the OPC Unified Architecture (OPC UA) information model. The presented InPro ontology was further evaluated by a hybrid combination of approaches. Additionally, the InPro ontology was implemented with practical use cases to support production planning and failure analysis by retrieving relevant information via SPARQL queries. The validation results also demonstrated that using the proposed InPro ontology allows for efficiently formalizing, integrating, and retrieving information within the industrial production process context.