MAShES | Multimodal spectrAl control of laSer processing with cognitivE abilities

Summary

MAShES proposed a breakthrough approach to image-based laser processing closed-loop control. Firstly, a compact, snapshot, and multispectral imaging system in the VIS/MWIR spectral range was developed. This approach enabled a multimodal process observation that combines different imaging modalities. Moreover, it enabled an accurate estimation of temperature spatially resolved and independent on emissivity values, even for non-grey bodies. Secondly, a fully embedded approach to real time (RT) control was adopted for efficient processing of acquired data and high speed -multiple inputs/ multiple outputs- closed-loop control. Thirdly, a cognitive control system based on the use of machine learning techniques applied to process quality diagnosis and self-adjustment of the RT control was developed.

As a result, a unified and compact embedded solution for RT-control and high speed monitoring was developed that brings into play:

  • The accurate measurement of temperature distribution,
  • The 3D seam profile and 2D melt pool geometry,
  • The surface texture dynamics, and process speed.

MAShES control act simultaneously on multiple process variables, including laser power and modulation, process speed, and spot size. MAShES also delt with usability and interoperability issues for compliance with cyber-physical operation of the system in a networked and cognitive factory. Moreover, standardisation issues were addressed regarding the processes and the control system . MAShES system was designed under a modular approach, easily customizable for different laser processing applications in highly dynamic manufacturing scenarios. Validation and demonstration of prototypes of MAShES system were done for laser welding and laser metal deposition (LMD) in operational scenarios at representative end-user facilities.

More information & hyperlinks
Web resources: http://www.mashesproject.eu
https://cordis.europa.eu/project/id/637081
https://www.youtube.com/channel/UCQp4Ubw_XB4OiTWT9vQLhzQ - You Tube channel
Start date: 01-12-2014
End date: 30-11-2017
Total budget - Public funding: 3 673 157,00 Euro - 3 673 157,00 Euro
Cordis data

Original description

MAShES proposes a breakthrough approach to image-based laser processing closed-loop control.

Firstly, a compact, snapshot, and multispectral imaging system in the VIS/MWIR spectral range will be developed. This approach will enable a multimodal process observation that combines different imaging modalities. Moreover, it will enable an accurate estimation of temperature spatially resolved and independent on emissivity values, even for non-grey bodies and dissimilar materials. Secondly, a fully embedded approach to real time (RT) control will be adopted for efficient processing of acquired data and high speed -multiple inputs/ multiple outputs- closed-loop control. Thirdly, a cognitive control system based on the use of machine learning techniques applied to process quality diagnosis and self-adjustment of the RT control will be developed.

As a result, a unified and compact embedded solution for RT-control and high speed monitoring will be developed that brings into play:
- The accurate measurement of temperature distribution,
- The 3D seam profile and 2D melt pool geometry,
- The surface texture dynamics, and process speed.

MAShES control will act simultaneously on multiple process variables, including laser power and modulation, process speed, powder and gas flow, and spot size.

MAShES will deal with usability and interoperability issues for compliance with cyber-physical operation of the system in a networked and cognitive factory. Moreover, standardisation issues will be addressed regarding the processes and the control system and contributions in this regard are envisaged.

MAShES will be designed under a modular approach, easily customizable for different laser processing applications in highly dynamic manufacturing scenarios. Validation and demonstration of prototypes of MAShES system will be done for laser welding and laser metal deposition (LMD) in operational scenarios at representative end-user facilities.

Status

CLOSED

Call topic

FoF-01-2014

Update Date

27-10-2022
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Factories of the Future Partnership - Made in Europe Partnership

H2020 - Factories of the Future
H2020-FoF-2014
FoF-01-2014 Process optimisation of manufacturing assets
Research & Innovation Action (RIA)
Economic sustainability
Comment:
Flexibility
Environmental sustainability
Waste minimisation
Reduction of waste (in %)
20
Comment:

A reduction of at least 20% of defective parts produced by additive manufacturing by CNC LMD, that mus be discarded.

A reduction of at least 50%of defective repairing by robotic LMD, that must reworked to be repaired again .

Information and communication technologies
IoT - Internet of Things
Advanced material processing technologies
Photonics-based materials processing technologies
Mechatronics and robotics technologies
Measurement, sensing, condition and performance monitoring technologies
Control technologies
Intelligent machinery components, actuators and end-effectors
C MANUFACTURING
C25 Manufacture of fabricated metal products, except machinery and equipment
C28 Manufacture of machinery and equipment n.e.c.
C29 Manufacture of motor vehicles, trailers and semi-trailers
C30 Manufacture of other transport equipment
C30.3 Manufacture of air and spacecraft and related machinery
Horizon 2020
H2020-EU.2. INDUSTRIAL LEADERSHIP
H2020-EU.2.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
H2020-EU.2.1.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)
H2020-EU.2.1.1.0. INDUSTRIAL LEADERSHIP - ICT - Cross-cutting calls
H2020-FoF-2014
FoF-01-2014 Process optimisation of manufacturing assets
H2020-EU.2.1.5. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Advanced manufacturing and processing
H2020-EU.2.1.5.1. Technologies for Factories of the Future
H2020-FoF-2014
FoF-01-2014 Process optimisation of manufacturing assets