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.

RADICLE aims to create a real-time dynamic control system for laser welding using a combination of (application specific) sensors in combination with intelligent and predictive control technologies for in-process monitoring and control to minimize/eliminate defects for a range of materials and geometries for both aerospace, automotive and other applications. The control system will include pre- and post-welding measurement as well as in-process monitoring, control and fault prevention / fixing. The project has 4 large, end-user partners - Rolls-Royce, Alstom, GKN and CRF (FIAT) therefore the impact of RADICLE is expected to be very large. The overall impacts of successful implementation of the RADICLE technology through our consortium and the wider welding sectors will enable us to achieve the following impacts: - Increased productivity of up to 30%, resulting in: - 30% reduced energy usage; - 30% reduced emissions; - Eliminate the need for part scrappage or rework (up to 20%-30% of labour input); - Reduction or removal of the need for final NDE testing of the parts; - Remove need for large enclosed remote welding rooms (~35% floor space reduction); - Increase health and safety benefits; In addition, RADICLE will contribute to the wider Europe 2020 targets, through: - Increased employment of 20-64 year-olds; - Increased R&D spending; - Reduced energy usage and greenhouse gas emissions; - Increased education, especially at third level education;