Knowledge-based multi-level aggregation for decision aid in the machining industry

TL;DR

This paper introduces a knowledge-based multi-level data aggregation method to enhance decision-making in manufacturing, effectively managing Big Data challenges in Industry 4.0 environments.

Contribution

It proposes a novel multi-level aggregation strategy that incorporates manufacturing knowledge at each level, improving data analysis efficiency for decision support.

Findings

Successfully applied to an aeronautic machining database

Enhanced decision-making capabilities in manufacturing processes

Reduced computational load through smart data aggregation

Abstract

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