A methodology for Digital Twin-based lifecycle management to support adaptive (dis-)assembly planning in circular repair factories

The increasing relevance of circular manufacturing and repair oriented production requires adaptive and data-driven process planning approaches capable of handling highly variable products, uncertain component conditions, and incomplete process information. Conventional automation methods for assembly and disassembly are typically optimized for repetitive high volume manufacturing and are therefore not directly suitable for flexible repair environments. This work proposes a methodology for Digital Twin-based lifecycle management to support adaptive (dis-)assembly planning in circular repair factories. The methodology integrates heterogeneous lifecycle data into a unified Digital Twin environment and utilizes simulation-driven methods to evaluate robotic grasp feasibility, assembly accessibility, end effector suitability, and process constraints prior to physical execution. The novelty of this work lies in integrating lifecycle data, adaptive execution strategies, and simulation-driven planning into a unified framework for flexible circular repair operations. The framework combines robotic grasp planning, simulation-based (dis-)assembly analysis, and decision-making for robot-guided, collaborative, or manual execution. Lifecycle data further enhances efficiency through data-driven optimization methods such as assembly group and geometry-based grasp planning. While prototypical implementations are provided for robotic grasp planning and optimization methods, the overall lifecycle-driven framework and execution decision strategies remain conceptual. Validation results demonstrate grasp feasibility analysis, quantitative comparison of robot and end effector configurations, and geometric approximation through manufacturing data integration. Overall, the implemented modules indicate the potential to increase process success, and reduce replanning effort for flexible circular repair processes.