Knowledge-based multi-level aggregation for decision aid in the machining industry
This addresses data management challenges for decision-making in the machining industry, specifically in aeronautics, but appears incremental as it builds on existing aggregation methods with knowledge integration.
The paper tackles the Big Data issue in manufacturing by proposing a knowledge-based multi-level aggregation strategy to generate smart data for decision aid, and it was successfully applied to a real machining database from the aeronautic industry.
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