Objective
Objectives and content
The proposed MAIL project is aimed at basic improvement in the reliability of micro-spot laser welding of metal parts in production, by the deveopment of a self-learning real-time feedback control system.
The basic problem of this technology is how to attain the Par tPerMillion PPM) defect level needed for wider industrial application in micro-mechancs and electronics. The two most important defect sources are addressed in this project:
- Defects due to position tolerances of the products: an "Aim and Shoot" system that recognizes the joint position and adjusts the actual laser spot position will be developed.
- Defects resulting from the variation in surface conditions of the parts: a generic multi-sensor realtime monitoring and the following modules will be developed:
- advanced pattern recognition for joint classification (allowing process feedback through the line operator),
- adaptive control, based on process modelling ant Neural Network self learing,
- real-time closed loop control of the beam energy. The to be developed system has a modular architecture, the monitoring part can be combined with one of the three control modules, depending on application.
This modularity will allow customised applications, covering several market segments. The long term prospect of this development is to partly replace the present soldering technology by the laser spot welding technique: clean production and green products. The 1st phase -Process Identificationcontains investigation into the relevant physieal parameters for joint assessment & classification. In the 2nd phase - Closed Loop Control - adaptive feedback as well as closed-loop control during the laser pulse will be developed. The Neural Networks module will allow, through its self-learning capability, quick start-up of a new application In the 3rd phase - Testbed Applications -the developed system will be tested in three prototype test-beds.
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.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering control systems
- natural sciences computer and information sciences artificial intelligence pattern recognition
- natural sciences physical sciences optics laser physics
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
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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.
Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
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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.
Coordinator
5600 MD Eindhoven
Netherlands
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.