The second period of the project concentrated in the definition of the OPeraTIC system architecture, at hardware, software and data level, and the specification of a complete platform including laser optical path, motion system and motion control. After this work, project partners developed a number of individual components for the OPeraTIC system, taking into account their integration. Advanced versions of the different components and modules were delivered as follows:
- Laser Source: High energy (1 mJ, 100W) picosecond IR laser, including advanced pulse-to-pulse triggering with sub-20 ns jitter and pulse/train on demand capabilities, with electronics responsive up to 50 MHz bandwidth.
- Optical Fibre: prototype for the fibre and beam launching system (BLS), demonstrated to be able to transmit femtosecond pulses with high energy (up to 1mJ for 300 fs pulses), and a Polarization Maintaining version able to transmit up to >200µJ pulses keeping predictable linear polarization with <30% losses.
- Beam Shaping modules: DLIP module (interference patterning) providing several millimetres in Depth of Focus, facilitating the usage on curved and complex 3D surfaces. DLW module consisting of an optical setup with an LCoS-SLM optical engine to be combined with industrial class collimators and galvoscanners, to produce custom energy distribution on demand in a robust and automated fashion.
- Robotic manipulator: robust, large volume (1m3), large area (>1m2) working space robotic system, including a novel motion controller programmed from scratch for dynamically corrected interpolated seven axis (five physical plus two optical axis) motion for on-the fly laser processing on large 3D surfaces.
- Beam and process monitoring system for DLIP and DLW, detecting beam quality issues, misalignments and other optical path deviations. Diffractographic and scatterometric quality control tool.
- Machine-level logical and data components, including Asset Administration Shell, full implementation of a Middleware for data handling, storage, serving, visualization and processing, and all required connectors for signal and information exchange in the system.
- AI models and algorithms able to train with a minimal amount of test data, thanks to a base training with synthetic information obtained through Physically Informed models, plus a Transfer Learning strategy. From those models and algorithms, predictive, prescriptive and corrective capabilities will augment the machine control to implement FTR and ZDM production strategies.
