A failure-driven and knowledge-enhanced self-planning method for disassembly process reconstruction

TY - JOUR

T1 - A failure-driven and knowledge-enhanced self-planning method for disassembly process reconstruction

AU - Ren, Yaping

AU - Ren, Xinyi

AU - Sun, Xiaoguang

AU - Zhuang, Cunbo

AU - Wu, Jianzhao

N1 - Publisher Copyright: © 2025 Informa UK Limited, trading as Taylor & Francis Group.

PY - 2026

Y1 - 2026

N2 - Disassembly plays a crucial role in the recycling and remanufacturing of retired electromechanical products. However, the various structural damages of subassemblies sometimes results in disassembly failures, such as fracture, wear, and corrosion. Uncertain disassembly failures lead to complex process reconstruction, which involves updating information, reconfiguring elements, and replanning sequences. Thus, this study proposes a failure-driven and knowledge-enhanced self-planning method for disassembly process reconstruction. First, a failure-based disassembly knowledge graph is constructed, which integrates various types of disassembly failure knowledge and supports disassembly information updates. Then, the multi-dimensional disassembly elements are reconfigured through rule-based reasoning and logical reasoning, on the basis of which three reconstruction strategies are proposed, and the disassembly sequences under disassembly failures are rapidly replanned by a reconstruction strategy selection-based genetic algorithm. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-planning method. Experimental results demonstrate the method's effectiveness in reconstructing disassembly processes under various failure types and degrees, limiting profit reduction to below 7%. The hybrid strategy significantly outperforms single strategies in managing multiple failures, and shows superior performance over PSO and ABC algorithms in solving complex disassembly planning problems.

AB - Disassembly plays a crucial role in the recycling and remanufacturing of retired electromechanical products. However, the various structural damages of subassemblies sometimes results in disassembly failures, such as fracture, wear, and corrosion. Uncertain disassembly failures lead to complex process reconstruction, which involves updating information, reconfiguring elements, and replanning sequences. Thus, this study proposes a failure-driven and knowledge-enhanced self-planning method for disassembly process reconstruction. First, a failure-based disassembly knowledge graph is constructed, which integrates various types of disassembly failure knowledge and supports disassembly information updates. Then, the multi-dimensional disassembly elements are reconfigured through rule-based reasoning and logical reasoning, on the basis of which three reconstruction strategies are proposed, and the disassembly sequences under disassembly failures are rapidly replanned by a reconstruction strategy selection-based genetic algorithm. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-planning method. Experimental results demonstrate the method's effectiveness in reconstructing disassembly processes under various failure types and degrees, limiting profit reduction to below 7%. The hybrid strategy significantly outperforms single strategies in managing multiple failures, and shows superior performance over PSO and ABC algorithms in solving complex disassembly planning problems.

KW - Retired electromechanical products

KW - disassembly failure

KW - disassembly process reconstruction

KW - knowledge-enhanced

KW - rule-based reasoning

UR - https://www.scopus.com/pages/publications/105019657735

U2 - 10.1080/00207543.2025.2575842

DO - 10.1080/00207543.2025.2575842

M3 - Article

AN - SCOPUS:105019657735

SN - 0020-7543

VL - 64

SP - 5095

EP - 5119

JO - International Journal of Production Research

JF - International Journal of Production Research

IS - 12

ER -