Selective disassembly planning considering process capability and component quality utilizing reinforcement learning
Paper in 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, Industrial and Materials Science, Product Development

Maria Chiara Magnanini

Polytechnic University of Milan

Florian Stamer

Karlsruhe Institute of Technology (KIT)

Marvin Carl May

Karlsruhe Institute of Technology (KIT)

Gisela Lanza

Karlsruhe Institute of Technology (KIT)

Kristina Wärmefjord

Chalmers, Industrial and Materials Science, Product Development

Rikard Söderberg

Chalmers, Industrial and Materials Science, Product Development

Procedia CIRP

22128271 (ISSN)

Vol. 121 1-6

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

AI-based intelligent disassembly utilizing a Digital Twin (AIDDT)

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

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Reliability and Maintenance

Computer Science

Areas of Advance




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