In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry. Decision Making; Machining; Knowledge based system
@article{arxiv.1905.06413,
title = {Knowledge-based multi-level aggregation for decision aid in the machining industry},
author = {Mathieu Ritou and Farouk Belkadi and Zakaria Yahouni and Catherine Da Cunha and Florent Laroche and Benoit Furet},
journal= {arXiv preprint arXiv:1905.06413},
year = {2019}
}