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Multimodal spectral sensors and orchestrated deep models for integrated process optimisation

Project description

Spectral sensor for integrated process optimisation

The EU-funded MULTIPLE project aims to develop cost-effective multimodal monitoring solutions with a breakthrough impact on production quality and efficiency. It hopes to bring cutting-edge organic electronics-based sensors, snapshot hyperspectral filters and dual-aperture imaging to deliver cost-effective spectrometers and camera cores in a broad VIS/SWIR range. This will also be complemented with cost effective laser-based chemometrics. The objective is to leverage SoA cloud and big data technologies to create new services and microservices supporting data collection, deep learning model development, and enhanced decision support tools.

Objective

MULTIPLE will develop cost-effective multimodal monitoring solutions with a breakthrough impact on production quality and efficiency. It will bring cutting-edge OLED-based sensors, snapshot hyperspectral filters and dual-aperture imaging to deliver cost-effective spectrometers and camera cores in a broad VIS/SWIR range, complemented with cost effective laser-based chemometric sensors in the MWIR. On top of these sensors, MULTIPLE will develop novel multimodal monitoring systems that will be IoT native, by combining them with cloud, big data, and deep learning for agile development and orchestration of complex AI-based models to optimize production improving EU manufacturing competitiveness.
To develop cost-effective HSI SWIR (0.9-1.7 µm) camera cores ready for volume production.
To develop a compact and cost-effective dual-aperture HSI imager combining snapshot CMOS and InGaAs hyperspectral sensors based on mosaic filters covering the whole VIS/SWIR range (0.4-1.7 µm).
To develop novel cost-effective spectrometer solutions for highly specific chemometric analysis in an extended VIS/MWIR range (0.4-3.5 µm wavelength), based on innovative OLED sensors and laser-based spectroscopy.
To develop embedded deep models for regression, classification and RT control. It will guarantee onsite and online measurements and classification of different types of categories with high flexibility, accuracy, and reliability.
To develop an integral and scalable approach to process monitoring, quality assurance, and process optimization through the orchestration of photonic monitoring systems under an IoT native approach. The aim is to leverage SoA cloud and big data technologies to create new services and microservices enhancing decision support tools.
To demonstrate MUTLIPLE in 3 manufacturing scenarios: steelworking, woodworking and food

Call for proposal

H2020-ICT-2018-20

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Sub call

H2020-ICT-2019-2

Coordinator

ASOCIACION DE INVESTIGACION METALURGICA DEL NOROESTE
Net EU contribution
€ 799 374,99
Address
CALLE RELVA TORNEIROS 27A
36410 Porrino
Spain

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Region
Noroeste Galicia Pontevedra
Activity type
Research Organisations
Links
Total cost
€ 799 375,00

Participants (19)