Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/61373
Title: | Predicting the printed circuit board cycle time of surface-mount-technology production lines using a symbiotic organism search-based support vector regression ensemble |
Contributor(s): | Li, Debiao (author); Chen, Siping (author); Chiong, Raymond (author) ; Wang, Liting (author); Dhakal, Sandeep (author) |
Publication Date: | 2021 |
DOI: | 10.1080/00207543.2020.1837407 |
Handle Link: | https://hdl.handle.net/1959.11/61373 |
Abstract: | | This paper presents a symbiotic organism search (SOS)-based support vector regression (SVR)ensemble for predicting the printed circuit board (PCB) cycle time of surface-mount-technology(SMT) production lines. Being able to predict the PCB cycle time accurately is essential for optimis-ing the SMT production schedule. Although a machine simulator can be reliably used for single-type PCB production, it is time-consuming and often inaccurate for the simulator to be applied for highlymixed orders in multiple flexible SMT production lines. Due to the dynamic changes in both PCBorders and SMT production lines, there is a diverse set of samples, but the size of similar samples isrelatively small. An SVR model is therefore used to estimate the PCB cycle time, and the SOS algorithmis employed to optimise the SVR parameters. We assume that uncertainties during the assembly process can be captured by the characteristics of PCB and SMT lines, which are utilised as features to train the SVR model. To enhance the performance of the prediction accuracy, an SOS-SVR ensembleis proposed. Experiments based on datasets collected from a leading global electronics manufacturer confirm the efficiency of the proposed approach compared to industrial solutions currently in place and other machine learning methods.
Publication Type: | Journal Article |
Source of Publication: | International Journal of Production Research, 59(23), p. 7246-7265 |
Publisher: | Taylor & Francis |
Place of Publication: | United Kingdom |
ISSN: | 1366-588X 0020-7543 |
Fields of Research (FoR) 2020: | 4602 Artificial intelligence |
Peer Reviewed: | Yes |
HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
Appears in Collections: | Journal Article School of Science and Technology
|
Files in This Item:
1 files
Show full item record
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.