Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs
Petr Novak, Stefan Biffl, Marek Obitko, Petr Kadera
TL;DR
The paper addresses the challenge of analyzing undesired conditions in flexible CPPS under Industry 4.0 variability. It introduces PPR-AKG, an OWL-based extension of the PPR framework that models undesired conditions and their causes, structured to map required versus provided capabilities for dynamic resource allocation. By integrating LLM-based chat interfaces, the approach enables intuitive querying, reasoning, and instantiation of concepts within the ontology. The EV battery remanufacturing use case demonstrates improved resource allocation and condition mitigation, validating practical relevance while identifying validation and optimization challenges. Overall, the work presents a holistic, ontology-driven, human-centered method to enhance quality assurance in flexible CPPS.
Abstract
Contemporary industrial cyber-physical production systems (CPPS) composed of robotic workcells face significant challenges in the analysis of undesired conditions due to the flexibility of Industry 4.0 that disrupts traditional quality assurance mechanisms. This paper presents a novel industry-oriented semantic model called Product-Process-Resource Asset Knowledge Graph (PPR-AKG), which is designed to analyze and mitigate undesired conditions in flexible CPPS. Built on top of the well-proven Product-Process-Resource (PPR) model originating from ISA-95 and VDI-3682, a comprehensive OWL ontology addresses shortcomings of conventional model-driven engineering for CPPS, particularly inadequate undesired condition and error handling representation. The integration of semantic technologies with large language models (LLMs) provides intuitive interfaces for factory operators, production planners, and engineers to interact with the entire model using natural language. Evaluation with the use case addressing electric vehicle battery remanufacturing demonstrates that the PPR-AKG approach efficiently supports resource allocation based on explicitly represented capabilities as well as identification and mitigation of undesired conditions in production. The key contributions include (1) a holistic PPR-AKG model capturing multi-dimensional production knowledge, and (2) the useful combination of the PPR-AKG with LLM-based chatbots for human interaction.