Auto-Twin project partner: Core

Articles, News

June 16, 2023

The transition towards a Circular Economy is estimated to represent a $4.5 trillion global growth opportunity by 2030. Digital Twins availability is largely recognized as an accelerator and an enabler of Circular Economy in business and in production, but significant challenges are still standing in relation to its development within the present technological framework, the needed skill sets, and the implementation costs.

AUTO-TWIN addresses the technological shortcoming and economic liability of the current system engineering model by:

1) introducing a breakthrough method for automated process-aware discovery towards autonomous Digital
Twins generation
, to support trustworthy business processes in circular economies;

2) adopting an (International Data Space) IDSbased common data space, to promote and facilitate the secure and seamless exchange of manufacturing/product/business data within value-networks in a circular-economy ecosystem;

3) integrating novel hardware technologies into the digital thread, to create smart Green Gateways, empowering companies to perform data and digital twin enabled green decisions, and to unleash their full potential for actual zero-waste Circular Economy and reduced dependency from raw materials.

The Auto-Twin project

AUTO-TWIN vision is addressing four specific objectives, each one solving a well-defined research challenge,
resulting in measurable exploitable results, used as clear and realistic indicators for the achievement of the project goals. These objectives, and their underlying research questions, will guide all the activities of the work plan of this Research and Innovation action. AUTO-TWIN concept is agnostic and domain independent.

OBJECTIVE 1 – Automated digital twin generation, operations, and maintenance in circular value chains.

AUTO-TWIN will develop a novel data-driven method based on a process mining approach for generation and adaptation of multi-fidelity resolution digital twins from data acquired, at multiple levels, along the value chain. Full automation, trustworthiness, and “skill-free” play are key-selling points of the project approach, based on sparse traces of manufactured objects flowing in the value chain. The project will rely on multiple automated actors to discover the flows in the value chain and to automatically generate and update digital twins of complex business processes, starting from the specific manufacturing processes till the whole supply chain.

OBJECTIVE 2 – Trustworthy high resolution track & trace of products and processes among different actors in circular value chains

The consistency of data and information sharing among stakeholders across the value network are fundamental for the validity of digital twins. AUTO-TWIN aims to: i) enable high resolution track&tracing through precise and detailed data and information about products and processes; ii) manage data and access rights in a seamless, secure, powerful and flexible way; iii) manage data sharing and sovereignty within circular data spaces. AUTO-TWIN will define, implement and test its systems and solutions in real business environments in very different sectors to define business guidelines and best practices and share them with the community.

ai4manufacturing|digital twins manufacturing premises

OBJECTIVE 3 – Reduce skills and knowledge gaps for all involved actors through augmented intelligence.

In order to perform significant progress in the state-of-art, the design of a new approach for assessing skills and mapping production processes is a mandatory requirement. It should be reliable, complete and built on standardized frameworks. The AUTO-TWIN project answers to this requirement developing: (i) Explainability for Artificial Intelligence (XAI) techniques able to decrease the required skills; (ii) tools to assess the current skills of the single worker in a specific production context and (iii) to identify (through a data-driven approach) the most efficient upskilling paths for operators as well as their related production processes.

OBJECTIVE 4 – Augmented intelligence algorithms for decision making at Green Gateways.

AUTO-TWIN will define new methods and algorithms to extend knowledge and support actors in decision-making at Green Gateways. Artificial Intelligence techniques and process mining algorithms will be developed to extract significant correlations, then transformed into explainable knowledge, underlying the product data along their life cycles and their manufacturing and logistic processes as well as the dependency of the demand on pricing and product characteristics. This knowledge, together with digital twin predictions on relevant multidimensional indicators (i.e., circularity indices, productivity indices, supply chain indices) will be used with augmented knowledge and real time production data for optimal coordination of the value chain under stochastic behavior


The AUTO-TWIN Consortium is composed by 13 partners and associated entities across 7 countries.

CORE Innovation Centre is a non-profit RTO, subsidiary of CORE Innovation & Technology OE, aiming to provide individuals, industries and companies with opportunities to reach their true potential, to make industries smarter and greener, more sustainable and more socially inclusive. Industry 4.0 is where CORE IC chooses to focus its research and innovation efforts. Advances in a range of technologies i.e. IoT, big data, machine and deep learning, edge and cloud computing are deployed to maximise the potential of both people and entities in multiple sectors.

Core focuses on industries that want to take their productivity and sustainability to the next level by incorporating Machine Learning and Artificial Intelligence technologies in the field of Predictive Maintenance and Energy Forecasting & Optimisation.

Core target industries include Industry 4.0 & Manufacturing, Energy (EeB, Smart Grids), Critical Infrastructure and Equipment, Smart Systems and Personalisation, and Smart Cities and Communities.

The role of CORE in the Auto-Twin project

Technology Provider

Core is a fast-growing SME, founded in 2016, focusing on harnessing data to provide tailor made
machine-learning algorithms for predictive maintenance and smart energy management in industrial applications, such as manufacturing, energy, critical infrastructure, and smart systems. Core will raise awareness about AUTO-TWIN by leading the dissemination, exploitation and communication activities, leading the project towards other existing projects via reliable communication media. Core will lead the AI-empowering of Green Gateways, and the early solution customisation phase.

This project has received funding from the Horizon Europe programme under the Grant Agreement No. 101092021

This article has been extracted and freely adapted from the Auto-Twin official website and Proposal.

Project Coordinator: Andrea Matta, full professor of Processing Technologies and Systems at the Mechanics Department of the Politecnico di Milano