KAPPS: A knowledge-based CPPS Architecture for the Circular Factory

2026-05-21

AI Summary

Traditional manufacturing IT architectures struggle to address the challenges posed by heterogeneous product states, dynamically reconfigurable processes, and the integration of human–machine knowledge in circular economy contexts. This work proposes a knowledge-driven cyber-physical production system architecture that elevates an ontology-backed knowledge graph from an integration layer to the authoritative source of truth for factory runtime states. A semantic interface layer enables data integration, reasoning, and communication across heterogeneous systems. The architecture incorporates constraint enforcement and event-driven planning mechanisms to support incremental replanning under uncertainty and collaborative human–machine decision-making. Validation through two use cases— anomaly detection learning and constraint enforcement in modular conveyor systems—demonstrates effective fulfillment of all 14 specified design requirements.

Abstract

While linear manufacturing relies on homogeneous materials and predefined process sequences, circular manufacturing reintroduces used products with heterogeneous and uncertain conditions. This shift demands manufacturing systems capable of handling variable product states, dynamically reconfigurable processes, and the integration of human and machine knowledge. Conventional manufacturing IT architectures, designed for stable structures and deterministic execution, are unable to meet these requirements, as they cannot adequately represent and manage the uniqueness of individual components at runtime. Following a design science methodology for developing a Cyber Physical Production System for circular manufacturing, we derive 14 requirements from five complementary perspectives. Based on these requirements, we design KAPPS, a knowledge-based architecture that uses an ontology-grounded knowledge graph as a unifying data backbone, combined with a semantic interface layer to enable consistent data and information integration, reasoning, and communication across heterogeneous systems and services, turning the knowledge graph from an integration layer into the factories authoritative write-time state. KAPPS incorporates modules for constraint enforcement and event-driven planning, enabling incremental adaptation of execution plans under uncertainty and human-machine knowledge exchange. The applicability of KAPPS is demonstrated through two implemented use cases: (i) Anomaly detection and learning through knowledge graph mediated services and (ii) runtime constraint enforcement in a modular conveyor system. Subsequently, the architecture is evaluated against the 14 requirements (ed. abstract shortened)

Problem

• circular manufacturing
• heterogeneous product states
• dynamic reconfiguration
• knowledge integration
• Cyber Physical Production System

Innovation

• knowledge graph
• circular manufacturing
• Cyber-Physical Production System
• ontology
• semantic integration