Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs

2025-08-08

AI Summary

In Industry 4.0, enhanced system dynamics in flexible Cyber-Physical Production Systems (CPPS) impede anomalous condition identification and undermine quality assurance mechanisms. To address this, we propose a Product–Process–Resource Asset Knowledge Graph (PPR-AKG) semantic modeling framework. This work pioneers the integration of OWL ontologies, knowledge graph technologies, semantic reasoning, and large language models (LLMs) to construct an end-to-end PPR-AKG model that enables natural-language-driven anomaly diagnosis and resource scheduling. The approach significantly enhances system interpretability and human–machine collaboration. Evaluated in an electric vehicle battery remanufacturing scenario, it achieves a 23.6% improvement in anomalous condition identification accuracy and a 31.4% increase in resource allocation efficiency. Moreover, it delivers real-time, intuitive, engineer-oriented interactive decision support—demonstrating practical applicability for industrial deployment.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Analyzing undesired conditions in flexible robotic production systems
• Improving quality assurance in Industry 4.0 cyber-physical systems
• Integrating semantic models with LLMs for intuitive factory interfaces

Innovation

• PPR-AKG model for flexible CPPS analysis
• Semantic tech and LLMs integration
• Natural language interaction for operators