Various realization methods of machine-part classification based on deep learning

Fangwei Ning (), Yan Shi (), Maolin Cai () and Weiqing Xu ()
Additional contact information
Fangwei Ning: Beihang University
Yan Shi: Beihang University
Maolin Cai: Beihang University
Weiqing Xu: Beihang University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 8, No 13, 2019-2032

Abstract: Abstract Parts classification can improve the efficacy of the manufacturing process in a computer-aided process planning system. In this study, we investigate various methodologies to assist with parts classification based on deep learning technologies, including a two-dimensional convolutional neural network (2D-CNN) trained using both picture data and CSV files; and a three-dimensional convolutional neural network (3D-CNN) trained using voxel data. Additionally, two novel methods are proposed: (1) feature recognition for the processing parts based on syntactic patterns, where their feature quantities are computed and saved to comma-separated variable (CSV) files that are subsequently employed to train the 2D-CNN model; and (2) voxelization of parts, wherein the voxel data of the parts is obtained for training the 3D-CNN model. The two methods are compared with a 2D-CNN model trained with the images of parts to classify. Results indicated that the 2D-CNN model trained with CSV data yielded the best performance and highest accuracy, followed by the 3D-CNN model, which was simpler and easier to implement and utilized better learning ability for the parts’ details. The 2D-CNN model trained with picture files evinced the lowest accuracy and a complex training network.

Keywords: Artificial intelligence; Artificial neural networks; Feature extraction; Industrial informatics; Optimized production technology; Classification algorithms; Image classification (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01550-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01550-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01550-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().