Selective disassembly planning considering process capability and component quality utilizing reinforcement learning
Paper i proceeding, 2024

Disassembly is a crucial process for achieving circular products, enabling function recovery, material reuse, and recycling. Disassembly planning is complex due to epistemic uncertainty associated with each unique product’s conditions, i.e., quality and aleatoric uncertainty about the capabilities of available resources and processes, and the cost benefits of associated operations impede planning. Therefore, the disassembly is intended to result in keeping the maximum value for the disassembled units of the product. In selective disassembly, the specification of the units of the product to be disassembled is acquired, leaving the rest of the product intact. The benefit of selective disassembly is to minimize waste during dismantling and maximize the reuse of the disassembled components for economic and ecological sustainability. The challenges in disassembly sequence planning include product complexity, operational and technological process capabilities, and the lack of information regarding the product architecture. For this complex planning task, limited studies have been performed on incorporating process capabilities with respect to the operations resources for selective disassembly planning. In this paper, an approach for optimal sequence planning of the selective disassembly process is put forward, taking into account multiple constraints, i.e., quality, time, and process capability. The intelligent planning approach takes advantage of a reinforcement learning model to handle the complexity of the planning problem. The approach has been implemented and tested on an industrial reference assembly. The result shows that the complex task of selective disassembly planning can be efficiently performed utilizing the proposed approach.

Reinforcement Learning



Selective Disassembly


Roham Sadeghi Tabar

Chalmers, Industri- och materialvetenskap, Produktutveckling

Maria Chiara Magnanini

Politecnico di Milano

Florian Stamer

Karlsruher Institut für Technologie (KIT)

Marvin Carl May

Karlsruher Institut für Technologie (KIT)

Gisela Lanza

Karlsruher Institut für Technologie (KIT)

Kristina Wärmefjord

Chalmers, Industri- och materialvetenskap, Produktutveckling

Rikard Söderberg

Chalmers, Industri- och materialvetenskap, Produktutveckling

Procedia CIRP

22128271 (ISSN)

Vol. 121 1-6

11th CIRP Global Web Conference, CIRPe 2023
Virtual, Online, ,

AI-baserad intelligent demontering med hjälp av en digitaltvilling (AIDDT)

VINNOVA (2022-02827), 2022-12-15 -- 2023-05-16.


Produktionsteknik, arbetsvetenskap och ergonomi

Tillförlitlighets- och kvalitetsteknik

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