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Industrial Data Services for Quality Control in Smart Manufacturing

01/01/2021 - 31/12/2023
  • Industry 4.0
  • Smart cities
Funded by





The i4Q project will provide a complete solution consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS) capable of managing the large amount of industrial data coming from low-cost, smart, small-sized, interconnected factory devices to support manufacturing, monitoring and online control of production.

The i4Q project will ensure data reliability with functions grouped into five core capabilities around the data cycle: sensing, communication, IT infrastructure, storage and analysis and optimization; based on a microservices-oriented architecture for end users.

With i4Q RIDS, factories will be able to handle large amounts of data, achieving appropriate levels of data accuracy, precision and traceability, using it for analysis and prediction, as well as to optimize process quality and product quality in manufacturing, leading to a defect-free manufacturing approach.


The objectives of i4Q are listed below:

O1: Develop and share among the different consortium partners and other stakeholders the i4Q Project Vision, establish the state of the art in terms of technologies for manufacturing quality and establish the requirements that drive the creation of i4Q Solutions. (WP1)

O2: Design the i4Q framework and Reference Architecture built on key digital models and ontologies for smart manufacturing and designed using multiple perspectives, related to business, usage, functional and implementation viewpoints. (WP2)

O3: Build i4Q manufacturing data quality by providing methodologies, tools and infrastructure to ensure the data quality necessary to enable operational intelligence and improve the effectiveness of data analysis results. (WP3)

O4: Create i4Q Manufacturing Data Analytics, a cloud-based lifecycle management toolset for manufacturing-related artificial intelligence models. (WP4)

O5: Build i4Q Rapid Manufacturing Line Reconfiguration and Qualification, a set of new and improved strategies and methods for process quality, as well as process reconfiguration and optimization using existing manufacturing data and machine learning algorithms. (WP5)

O6: Test and validate i4Q Solutions in 6 use cases, covering different manufacturing perspectives (industrial equipment manufacturers, parts and components manufacturers and final product manufacturers) and industrial sectors (metal, plastic, wood and ceramics). (WP6)

Success stories

A total of 6 pilots: https://www.i4q-project.eu/pilots-experiments


Partnersprofile (i4q-project.eu)

Technological capabilities

Big Data
Data analysis and exploitation