The Library
Object shape error correction using deep reinforcement learning for multi-station assembly systems
Tools
Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Dariusz (2021) Object shape error correction using deep reinforcement learning for multi-station assembly systems. In: 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 21-23 Jul 2021, Palma de Mallorca, Spain ISBN 9781728143958. doi:10.1109/INDIN45523.2021.9557359
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1109/INDIN45523.2021.9557359
Abstract
The paper proposes a novel approach, Object Shape Error Correction (OSEC), to determine corrective action in order to mitigate root cause(s) (RCs) of dimensional and geometric product shape errors. It leverages Deep Deterministic Policy Gradient (DDPG) algorithm to learn optimal process parameters update policies based on high dimensional state estimates of multi-station assembly systems (MAS). These policies can be interpreted in engineering terms as sequential corrective adjustments of process parameters that are necessary to mitigate RCs of product shape errors. The approach has the capability to estimate adjustments of process parameters related to fixturing and joining while simultaneously accounting for (i) RC uncertainty estimation, (ii) Key Performance Indicator (KPI) improvement, (iii) MAS design architecture; and, (iv) MAS inherent stochasticity. In addition, the OSEC methodology leverages a reward function parameterized by user interpretable functional coefficients for optimal tradeoff involving various corrections requirements. Benchmarking using an industrial, automotive cross-member assembly system demonstrates a 40% increase in the effectiveness of corrective actions when compared to current approaches.
Item Type: | Conference Item (Paper) | ||||
---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Publisher: | IEEE | ||||
ISBN: | 9781728143958 | ||||
Book Title: | 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) | ||||
Official Date: | 11 October 2021 | ||||
Dates: |
|
||||
DOI: | 10.1109/INDIN45523.2021.9557359 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) | ||||
Type of Event: | Conference | ||||
Location of Event: | 21-23 Jul 2021 | ||||
Date(s) of Event: | Palma de Mallorca, Spain |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |