Product Quality Improvements through Advanced Analytics

SCHEME: BRIDGES

CALL: 2018

DOMAIN: IS - Information and Communication Technologies

FIRST NAME: Riad

LAST NAME: Aggoune

INDUSTRY PARTNERSHIP / PPP: Yes

INDUSTRY / PPP PARTNER: ArcelorMittal Global R&D

HOST INSTITUTION: LIST

KEYWORDS: Predictive Modelling, Knowledge Mining, Quality Improvement, Industry 4.0

START: 2019-01-01

END:

WEBSITE: https://www.list.lu

Submitted Abstract

The setup of the process parameters in manufacturing is one of the most important steps required for delivering high-quality products. The objective of the PAX project is to close the gap currently not covered by classical quality control methods by (i) understanding and operationalizing the link between process parameters and product quality; and (ii) developing novel analytics tools and techniques that outperform traditional control processes. PAX will design an advanced automated learning system capable of monitoring and flagging process parameters with the aim of mitigating defects in a large array of manufacturing process. The outcomes of the PAX project will be integrated in a generic framework, in a first phase designed to investigate three use cases specific to ArcelorMittal, and to be extended at a later stage, beyond the steel industry and towards Industry 4.0 at large.

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