Affiliation:
1. School of Information Engineering Wuhan University of Technology Wuhan China
2. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology Wuhan China
Abstract
AbstractDisassembly is an important step in remanufacturing products. Robotic disassembly helps to improve disassembly efficiency. However, the end‐of‐life products often have the parts with uncertain quality, which is manifested as wear, fracture, deformation, corrosion, and other failure features. The parts failure features always have impacts on disassembly process. First, the evaluation method of parts failure features is researched, and the quantitative model of parts failure features is constructed using fuzzy models. Then, the disassembly information model is established by considering the influence of different failure degrees on the robotic disassembly process. Afterwards, to generate the optimal disassembly solution, deep reinforcement learning (DRL) is used to solve robotic disassembly sequence planning problem which considers parts failure features. Considering the influence of parts failure features on robotic disassembly time, the states, actions and rewards and environment are designed in DRL. Finally, a case study of the double shaft coupling as a waste product is carried out, and the proposed method is compared with the other methods to verify the effectiveness.
Publisher
Institution of Engineering and Technology (IET)
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Hardware and Architecture
Cited by
2 articles.
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