Product Digital Twin Supporting End-of-life Phase of Electric Vehicle Batteries Utilizing Product-Process-Resource Asset Network

In the context of the circular economy, products in their end-of-life phase should be either remanufactured or recycled. Both of these processes are crucial for sustainability and environmental conservation. However, manufacturers often do not support these processes enough by not sharing relevant data. This paper proposes use of a digital twin technology, which is capable to help optimizing the disassembly processes to reduce ecological impact and enhance sustainability. The proposed approach is demonstrated through a disassembly use-case of the product digital twin of an electric vehicle battery. By utilizing product digital twins, challenges associated with the disassembly of electric vehicle batteries can be solved flexibly and efficiently for various battery types. As a backbone for the product digital twin representation, the paper uses the paradigm of product-process-resource asset networks (PAN). Such networks enable to model relevant relationships across products, production resources, manufacturing processes, and specific production operations that have to be done in the manufacturing phase of a product. This paper introduces a Bi-Flow Product-Process-Resource Asset Network (Bi-PAN) representation, which extends the PAN paradigm to cover not only the manufacturing, but also the remanufacturing/recycling phase.

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

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.