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Automating the assembly planning process to enable design for assembly using reinforcement learning

Published online by Cambridge University Press:  16 May 2024

Rafael Parzeller ,
Dominik Koziol ,
Tizian Dagner  and
Detlef Gerhard
Rafael Parzeller*
Affiliation:
Siemens AG, Germany Ruhr-Universität Bochum, Germany
Dominik Koziol
Affiliation:
Siemens AG, Germany
Tizian Dagner
Affiliation:
Siemens AG, Germany
Detlef Gerhard
Affiliation:
Siemens AG, Germany
*
rafael.parzeller@siemens.com

Abstract

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This paper introduces a new concept for the automation of the assembly planning process, to enable Design for Assembly (DfA). The approach involves the application of reinforcement learning (RL) to assembly sequence planning (ASP) based on a 3D-CAD model. The ASP algorithm determines assembly sequences through assembly by disassembly. The assembly sequence is then used for the generation of subassemblies by considering the product contact information. The approach aims to support the creation of the manufacturing bill of materials (MBOM) by automating the assembly planning process.

Keywords

design for x (DfX)artificial intelligence (AI)assembly sequence planningsubassembly identification
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2179 - 2186
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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