In the realm of industrial automation and quality control, optical measurement techniques have long been the gold standard, offering swift and non-invasive inspections. However, these methods often come with a hefty price tag, both in terms of equipment costs and the specialized personnel required to operate them. Enter a groundbreaking study led by Maxim Polomoshnov from the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology (KIT) in Germany, which promises to revolutionize this landscape.
Polomoshnov and his team have developed a novel approach that leverages deep learning to conduct rapid measurements of printed linear structures. The standout feature of their method is the use of synthetic data generation to bypass the time-consuming process of collecting and assessing real-world training datasets. “We’ve essentially replaced the tedious data collection process with a digital sample generation technique, making the entire workflow faster, more flexible, and precise,” Polomoshnov explains.
The implications of this research are far-reaching, particularly for the energy sector. In functional printing, which is crucial for manufacturing solar cells, printed electronics, and other energy-related components, precise measurement of geometrical properties is paramount. The ability to conduct full-field analysis of non-preprocessed images using a neural network means that multiple geometrical properties can be measured simultaneously, significantly speeding up the production process and reducing costs.
Moreover, the versatility of this method means it can be easily adapted to a wide range of engineering fields. “Our concept is not limited to functional printing,” Polomoshnov notes. “It can be tailored to various applications where precise and rapid measurement of geometrical properties is required.”
The study, published in the International Journal of Optomechatronics (translated to English as the International Journal of Optics and Mechatronics), outlines the corresponding network architecture, workflows, and metrics used in this innovative approach. By integrating deep learning into optical measurement techniques, Polomoshnov and his team are paving the way for more efficient and cost-effective quality control processes in industrial automation.
As the energy sector continues to evolve, the demand for advanced manufacturing techniques that can keep pace with technological advancements grows ever more critical. This research not only addresses current challenges but also sets the stage for future developments in the field. By making optical measurement techniques more accessible and efficient, Polomoshnov’s work could play a pivotal role in shaping the future of industrial automation and energy production.