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)orcid ; 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

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