Generation of synthetic manufacturing datasets for machine learning using discrete-event simulation
Ka Ching Chan, Marsel Rabaev, Handy Pratama
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.