Generation of synthetic manufacturing datasets for machine learning using discrete-event simulation

Chan, K. C. ORCID: https://orcid.org/0000-0002-8756-2991 and Rabaev, Marsel and Pratama, Handy (2022) Generation of synthetic manufacturing datasets for machine learning using discrete-event simulation. Production & Manufacturing Research, 10 (1). pp. 337-353.

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Abstract

Recent advances in computing power have seen machine learning becoming an area of significant interest in manufacturing for scholars attempting to realise its full potential. Successful machine learning applications require a great amount of specific production data that is not easily nor publicly accessible. This study aims to develop a framework to use discrete-event-simulation (DES) to generate large datasets for training machine learning models. Three DES models were designed and executed to generate synthetic production data for different manufacturing scenarios. Inferences were made on the dependency between the time required to generate data and the complexity of the simulation model. The experimental results show that with the incremental changes in the simulation model, the time required to generate synthetic data tends to increase. The study revealed that DES is an effective tool for generating high-quality synthetic data which can be fed into machine learning models for training. The datasets generated by the simulations are made publicly available.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 20 Jul 2022 06:51
Last Modified: 20 Jul 2022 06:51
Uncontrolled Keywords: Discrete-event simulation; machine learning dataset; manufacturing process modelling; synthetic data generation
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461399 Theory of computation not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460199 Applied computing not elsewhere classified
40 ENGINEERING > 4014 Manufacturing engineering > 401407 Manufacturing management
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220499 Information systems, technologies and services not elsewhere classified
Identification Number or DOI: https://doi.org/10.1080/21693277.2022.2086642
URI: http://eprints.usq.edu.au/id/eprint/49926

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