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Point cloud object shape error datasets for root cause analysis of multi-station assembly systems
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Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Dariusz (2021) Point cloud object shape error datasets for root cause analysis of multi-station assembly systems. [Dataset]
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Official URL: http://doi.org/10.5281/zenodo.4537219
Item Type: | Dataset | ||||||
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Subjects: | Q Science > QA Mathematics T Technology > TS Manufactures |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Type of Data: | Modelling data | ||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Deep learning (Machine learning), Assembly-line methods | ||||||
Publisher: | Warwick Manufacturing Group | ||||||
Official Date: | 10 March 2021 | ||||||
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Status: | Not Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Copyright Holders: | University of Warwick | ||||||
Description: | Data record consists of a single zip archive containing the raw data. The dataset consists of supervised shape error datasets (point clouds) and corresponding process parameters. It is generated using the Variation Response Method (VRM) kernel. The dataset can be used for training deep learning frameworks to test performance for Root Cause Analysis (RCA) of Multi-Station Assembly Systems. The python library for implementation of the work can be found at this link: https://github.com/sumitsinha/Deep_Learning_for_Manufacturing |
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