Periodic Reporting for period 1 - METAMORPHA (Made-to-measure micromachining with laser beams tailored in amplitude and phase)
Période du rapport: 2022-09-01 au 2023-08-31
The METAMORPHA project was initiated with the goal of addressing the limitations and drawbacks of conventional manufacturing processes. The primary motivation behind the project is to provide a more sustainable and environmentally friendly solution for industrial production. Traditional micromachining processes often involve the use of chemicals, generate waste, and consume significant amounts of resources. This not only has a negative impact on the environment but also poses health and safety risks.
METAMORPHA aims to eliminate these issues by offering a single agile USP (ultrashort pulse) laser micromachining platform that can replace multiple conventional micromachining process chains. By utilizing an all-electric and all-digital approach, the concept eliminates the need for waste chemicals and reduces resource consumption.
The project introduces innovative features such as digital beam shaping and steering using two cascaded spatial light modulators (SLMs) and a galvo scanner. This allows for precise and versatile laser-based manufacturing processes, including surface structuring, polishing, drilling, and cutting. The module is designed to be compatible with standard industrial production lines and can be scaled up for parallel processing.
Improved efficiency and flexibility are to be achieved through novel measurement technologies and machine learning in process development. Each workpiece can be 3D-scanned and processed individually, ensuring a tailored laser micromachining process that maximizes efficiency and minimizes defects. Real-time sensors and machine learning algorithms provide in-line process control, allowing for adjustments and optimizations during production. All these properties make it possible to remanufacture worn or damaged tools, such as stamping or embossing dies, and make it a pioneering solution in the industry with respect to circular economy.
METAMORPHA aims to eliminate these issues by offering a single agile USP (ultrashort pulse) laser micromachining platform that can replace multiple conventional micromachining process chains. By utilizing an all-electric and all-digital approach, the concept eliminates the need for waste chemicals and reduces resource consumption.
The project introduces innovative features such as digital beam shaping and steering using two cascaded spatial light modulators (SLMs) and a galvo scanner. This allows for precise and versatile laser-based manufacturing processes, including surface structuring, polishing, drilling, and cutting. The module is designed to be compatible with standard industrial production lines and can be scaled up for parallel processing.
Improved efficiency and flexibility are to be achieved through novel measurement technologies and machine learning in process development. Each workpiece can be 3D-scanned and processed individually, ensuring a tailored laser micromachining process that maximizes efficiency and minimizes defects. Real-time sensors and machine learning algorithms provide in-line process control, allowing for adjustments and optimizations during production. All these properties make it possible to remanufacture worn or damaged tools, such as stamping or embossing dies, and make it a pioneering solution in the industry with respect to circular economy.
A thorough analysis of the requirements for the use cases (UCs) has been completed through collaboration with end users and relevant partners. Currently, data collection is underway to conduct a sustainability analysis of the current processes for all three UCs.
The project achieved successful ultrashort pulse (USP) laser machining of the major geometrical features within the specified tolerances in all three use cases (UCs) using a standard single beam machine which is the first of three stages for the process development. Additionally, a complete laser-based process chain (ablation, cleaning, polishing) for a carbide punch was demonstrated with 87% lower energy consumption than the state-of-the-art process chain.
An adapted training architecture for diffactive neural networks (DNNs) was utilized to calculate the phase masks of two cascaded spatial light modulators (SLMs) with arbitrary amplitude and phase distribution, within the physical limits. In laboratory experiments conducted with a low-power setup, the project successfully demonstrated high-quality beam shaping with both a single SLM and two cascaded SLMs. This achievement was recognized with the Best Student Paper award at the IODC 2023. A prototype is currently being built for a dual SLM module with a high-power ultrashort pulse (USP) source. For this setup, beam steering, manipulated angle of incidence and RMS deviation can be determined in ray optics simulation.
Regarding the development of the quality sensor, a comprehensive analysis of the use cases (UCs) has been conducted, focusing on the perspective of the quality sensor. Specifications for the sensor, including hardware and software requirements, have been defined. Currently, the project is in the process of integrating all the necessary elements for the successful implementation of the quality sensor into the system.
As part of the key objective to develop ML algorithms for real-time laser beam tailoring and process strategies, significant progress has been made in the project. This includes the automation of in-line sensor inputs for ML algorithms and the development of an initial ML model for surface roughness prediction based on sensor data, paving the way for real-time algorithm development.
Progress has been made towards achieving the key objective of "Edge-based in-line feedback process control based on real-time optical sensor data." A platform for real-time acquisition and ML-based analysis has been selected. Furthermore, necessary physical interfaces have been developed and real-time acquisition has been implemented and tested.
The development of the laser machine is on track. The selection of the laser source specifications ensures compatibility with the UC requirements. The determination of the physical size and axes configuration is nearly finalized. The machine will also be capable of accomodate fast rotating rollers for one UC. Additionally, the consortium is actively working on defining interfaces (mechanical, electrical, optical, PC etc.).
The project achieved successful ultrashort pulse (USP) laser machining of the major geometrical features within the specified tolerances in all three use cases (UCs) using a standard single beam machine which is the first of three stages for the process development. Additionally, a complete laser-based process chain (ablation, cleaning, polishing) for a carbide punch was demonstrated with 87% lower energy consumption than the state-of-the-art process chain.
An adapted training architecture for diffactive neural networks (DNNs) was utilized to calculate the phase masks of two cascaded spatial light modulators (SLMs) with arbitrary amplitude and phase distribution, within the physical limits. In laboratory experiments conducted with a low-power setup, the project successfully demonstrated high-quality beam shaping with both a single SLM and two cascaded SLMs. This achievement was recognized with the Best Student Paper award at the IODC 2023. A prototype is currently being built for a dual SLM module with a high-power ultrashort pulse (USP) source. For this setup, beam steering, manipulated angle of incidence and RMS deviation can be determined in ray optics simulation.
Regarding the development of the quality sensor, a comprehensive analysis of the use cases (UCs) has been conducted, focusing on the perspective of the quality sensor. Specifications for the sensor, including hardware and software requirements, have been defined. Currently, the project is in the process of integrating all the necessary elements for the successful implementation of the quality sensor into the system.
As part of the key objective to develop ML algorithms for real-time laser beam tailoring and process strategies, significant progress has been made in the project. This includes the automation of in-line sensor inputs for ML algorithms and the development of an initial ML model for surface roughness prediction based on sensor data, paving the way for real-time algorithm development.
Progress has been made towards achieving the key objective of "Edge-based in-line feedback process control based on real-time optical sensor data." A platform for real-time acquisition and ML-based analysis has been selected. Furthermore, necessary physical interfaces have been developed and real-time acquisition has been implemented and tested.
The development of the laser machine is on track. The selection of the laser source specifications ensures compatibility with the UC requirements. The determination of the physical size and axes configuration is nearly finalized. The machine will also be capable of accomodate fast rotating rollers for one UC. Additionally, the consortium is actively working on defining interfaces (mechanical, electrical, optical, PC etc.).
The project has achieved several results beyond the state of the art, including laser ablation of defined wall angles, a data acquisition system with real-time analysis, toolpath generation and control software, a part localization routine, the laser machine design, a robust design method for arbitrary systems of cascaded SLMs, a new kind of quality sensor and the corresponding post-processing software. METAMORPHA’s expected impact is unfolding as expected but it is too early to give details. These results will be further developed throughout the remainder of the project.