Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336841 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 37 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 237-255
Publisher: 
Springer US, New York, NY
Abstract: 
The increasing complexity of production environments and fluctuations in short-term demand requires adaptive and robust processes. To cope with the inherent challenges, deep reinforcement learning algorithms were widely deployed in assembly processes in recent years, due to their generalization capabilities, which ensure enhanced usability and flexibility for diverse assembly applications. Despite a growing number of scientific papers investigating deep learning based assembly and associated generalization capabilities, a comprehensive review and assessment of potential generalization capabilities has yet to be conducted. This paper aims to provide researchers and practitioners with an evaluation of key influences which contribute to a successful generalization of deep reinforcement learning within assembly processes, thereby facilitating further implementations. Our findings reveal that current research primarily focuses on examining generalization in insertion and sequence planning assembly tasks. Furthermore, we identified many context-specific approaches to enhance generalization, as well as remaining research challenges and gaps. The results comprise four overarching factors, containing several specific approaches that increase generalizability in assembly processes. However, future research must focus on verifying the context independence of these factors.
Subjects: 
Systematic literature review
Deep reinforcement learning
Machine learning
Generalization
Assembly
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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