Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time.

To address the data management limitations of traditional ontology models in dynamic industrial settings, this study introduces the Fractal Fuzzy Ontology Modeling (FFOM) framework, a novel methodology for optimizing data management, integration, and decision making. FFOM’s value is rooted in two major contributions: firstly, the strategic use of fractal structures to achieve unparalleled scalability and modularity, which significantly reduces the effort required during data hierarchy updates by enabling self-similar, expandable data architectures. Secondly, FFOM features the synergistic use of fuzzy logic to meticulously manage ambiguity and uncertainty, including the representation of imprecise relationships and support for flexible, rule-based reasoning. The practical value of this integrated approach is demonstrated through a mold assembly case study, which validates FFOM’s effectiveness in structuring complex data hierarchies, managing uncertainty, and enabling automated reasoning. Implemented in the Web Ontology Language (OWL) for standardization and interoperability purposes, FFOM ultimately provides a clear pathway toward developing more intelligent, adaptive, and scalable data ecosystems in demanding manufacturing domains, where real-time data analysis is critical.