Project description
Adaptive domain-deep transfer learning to predict CNC machine use
Computer numerical control (CNC) machines are crucial in manufacturing, but equipment failures lead to costly downtimes. Adapting models to different machines and handling data variability pose challenges. Supported by the Marie Skłodowska-Curie Actions programme, the PreAdapt-CNC project will develop an AI framework using advanced domain-deep transfer learning to predict component failures and assess the remaining useful life of CNC machines in industrial settings. The project will create an IoT framework, compile a fault dataset, develop a fast signal-extraction algorithm and design novel deep-learning models, with real-time testing and validation. The findings can help reduce unplanned downtime and extend equipment lifespan, enhancing European industrial competitiveness.
Objective
CNC machines have become an essential part of manufacturing industries. Unfortunately, unplanned downtime due to equipment
failure causes significant losses and disrupts production. Predictive maintenance using Artificial Intelligence (AI), particularly Deep
Learning (DL), offers a solution by handling complex data, extracting hidden correlations, and predicting failures accurately. However,
DL models often lack adaptability when applied to different machines or environments. Moreover, the complexities introduced by the
dynamic nature of machine operations, data variability, and multiple sensors pose significant challenges to implementing this
approach in real-time. Thus, I propose PreAdapt-CNC, a novel, robust, and adaptive AI framework incorporating adaptive domain
deep transfer learning, capable of accurately predicting component failures and remaining useful life for CNC machines under
industrial challenges. In this project, I will develop an IoT framework, a fault dataset for components, a fast signal and feature
extraction algorithm, novel DL models, and perform real-time testing and validation of the designed framework. My project will have
a significant economic impact by reducing unplanned downtime and increasing equipment lifespan. Furthermore, it aligns with the
EU strategy for the sustainable development goal of “Industry, Innovation, and Infrastructure,” boosting European industrial
competitiveness. For the project, Prof. Dimitrios Chronopoulos, a leading expert in vibration measurement, and failure prognosis at
KU Leuven, is the ideal supervisor. KU Leuven's proven track record in hosting Marie Curie fellows and managing research projects will
provide me with a cooperative environment. PreAdapt-CNC will advance my career through multidisciplinary skills, industrial
exposure, and specialized training. Moreover, I will also build a long-term collaboration network with European institutes, promoting
knowledge exchange, innovation, and future research.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences internet internet of things
- natural sciences computer and information sciences artificial intelligence machine learning transfer learning
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
3000 Leuven
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.