AI-Assisted Optical Measurement Technology for Sprays and Particles

ai-quanton GmbH (aiQ) develops advanced optical measurement technology for spray diagnostics, particle characterization, droplet measurement, and industrial process monitoring. We combine optical sensors, high-speed signal processing, dedicated measurement hardware, and artificial intelligence to develop new solutions for research and industrial applications.

In particular, aiQ focuses on research and pre-development of new measurement concepts, prototypes, analysis methods, and software platforms. Therefore, our work connects classical optical measurement techniques with modern AI-assisted data analysis.

AOM-Systems distributes and integrates commercial aiQ measurement products and solutions. Meanwhile, aiQ concentrates on scientific research, technological pre-development, new sensor concepts, and advanced data evaluation methods.

Our technology platforms include SprayQuantAI®, ParticleTensorAI®, and SprayConeAI®. Together, these technologies extend classical optical measurement methods and provide new possibilities for analyzing droplets, particles, spray cones, spray brushes, and dynamic industrial processes.

Our research focuses on extracting more information from optical measurement signals. Traditionally, optical measurement systems determine parameters such as droplet size, particle size, velocity, concentration, and spray distribution. However, modern signal processing and artificial intelligence can reveal additional information about the measured particles and droplets.

For this reason, aiQ develops hybrid measurement approaches that combine established physical measurement principles with machine learning and AI-based signal analysis. As a result, one measurement system can support both classical parameter determination and advanced material or process characterization.

These technologies support applications in fluid mechanics, aerodynamics, coating technology, atomization, spray processes, particle production, process development, and industrial process monitoring.

The name ai-quanton (aiQ) may appear new, but the technological development behind the company goes back more than a decade. aiQ was founded in 2013 as a spin-off from Technische Universität Darmstadt.

Before the company was founded, Walter Schäfer developed the first prototypes of the Time-Shift-Time-of-Flight (TSTOF) measurement technique during his PhD research between 2008 and 2012 [1,3]. Together with Prof. Cameron Tropea, he developed the technology further. The team presented an early prototype at ACHEMA 2012 in Frankfurt [2].

Already during this early development phase, the idea emerged to combine optical scattering measurements with intelligent data analysis. The aim was not only to detect droplets but also to characterize particles and more complex objects. At that time, however, practical AI tools and computing resources were still limited. Therefore, aiQ initially concentrated on classical signal-processing approaches.

During the spin-off process, the founders also established AOM-Systems. Within this collaboration, aiQ developed optical measurement instruments based on the TSTOF principle. One well-known measurement system used the brand name SpraySpy®. These instruments applied classical optical signal analysis methods described in the scientific work on TSTOF [3,4].

From 2021 onward, rapidly improving AI technologies opened new possibilities for optical measurement data. Consequently, aiQ intensified its research into machine-learning-assisted evaluation of light-scattering signals [5–7].

This development created the foundation for platforms such as SprayQuantAI®, ParticleTensorAI®, and SprayConeAI®. These technologies combine established measurement principles with new approaches to signal processing, image analysis, and artificial intelligence.

Today, aiQ continues to develop hybrid measurement concepts. We combine classical algorithms with AI-assisted analysis to address new scientific and industrial challenges in spray diagnostics, droplet measurement, particle characterization, and process monitoring.

ai-quanton develops customized software and hardware solutions for optical measurement systems, including technologies based on TSTOF and LDT. Our work focuses on the characterization of solid particles, transparent and non-transparent droplets, and complex sprays.

Researchers can use our technologies for experimental investigations, while industrial users can integrate them into process-monitoring applications. In this way, aiQ helps bridge the gap between laboratory measurements and real production environments.

SprayQuantAI® combines optical droplet detection with advanced signal analysis for dynamic spray monitoring. The technology supports the determination of important spray parameters and can also provide data for industrial monitoring and control systems.

ParticleTensorAI® analyzes complex light-scattering signals with artificial intelligence. Instead of reducing every detected event immediately to only a few numerical values, the approach preserves additional information from the optical signals for further AI-assisted analysis.

SprayConeAI® extends our technology portfolio toward web-based spray analysis. It supports the analysis of spray cones and spray patterns from images and therefore creates new possibilities for simple, scalable, and device-independent spray evaluation.

Based in Baden-Württemberg, Germany, aiQ develops integrated measurement and analysis solutions that combine precision hardware, optical sensing, dedicated software, and AI-assisted data analysis.

Moreover, our modular system architecture allows us to configure measurement instruments for different applications. Customers can access relevant measurement variables directly and connect the systems to existing industrial infrastructure.

Our software platforms primarily operate as stand-alone systems. In addition, clearly defined interfaces allow customers to connect gateways, cloud platforms, control systems, or external AI applications. This architecture provides flexibility for research installations as well as scalable industrial applications.

Scientific publications, experimental studies, and patent applications support the continuous development of our measurement technologies. Therefore, research and practical validation remain central parts of every new aiQ technology.

Since its foundation in 2013, aiQ has collaborated with universities, research institutes, technology companies, and industrial partners. These collaborations combine scientific research with practical measurement challenges.

Initially, the projects concentrated mainly on classical optical spray and particle measurement. Since 2021, however, artificial intelligence has become an increasingly important part of our research activities. Consequently, platforms such as SprayQuantAI®, ParticleTensorAI®, and SprayConeAI® now expand the analytical capabilities of TSTOF, LDT, and camera-based measurement systems.

In addition, joint measurement campaigns and publications help us test new concepts under realistic conditions. This close connection between scientific research and industrial applications supports continuous innovation and reliable technology development.

The following overview shows a selection of universities and industrial organizations with whom we have collaborated on scientific and applied research projects.

Our interdisciplinary team combines expertise in artificial intelligence, mathematics, optical sensing, electronics, hardware development, software engineering, user-interface development, and experimental research. Therefore, we can develop complete measurement concepts that connect sensors, electronics, algorithms, and practical applications.

Our AI development focuses on machine learning and artificial intelligence for optical measurement data. In particular, we investigate applications in fluid dynamics, aerodynamics, spray diagnostics, particle analysis, and intelligent signal evaluation.

Our hardware development covers electronic and sensor solutions for optical measurement instruments. The work includes sensor electronics, data acquisition, measurement interfaces, system integration, and robust hardware concepts for laboratory and industrial applications.

Our scientific advisory provides long-standing experience in algorithm development and optical flow measurement. This expertise includes work on technologies such as Particle Image Velocimetry. Today, this knowledge supports the transfer of established measurement concepts into modern AI-assisted diagnostic methods.

Our UI development combines web technologies, interface design, data visualization, and interactive analysis tools. As a result, users can access complex optical measurement data through clear and practical software interfaces.

Experimental research provides the foundation for our measurement technologies. We test new optical concepts with real sprays, droplets, particles, atomizers, and industrial processes. Consequently, experimental validation connects theoretical development with practical measurement applications.

The following publications, conference contributions, patents, and technical reports document important stages in the development of TSTOF and AI-assisted optical particle and droplet analysis.

[1] Schaefer, W.; Li, L.; Stegmann, P.; Terada, M. Technical Report on the TSTOF Measurement Method: Technical Basics, Historical Development, and Comparison with Other Laser-Based Measurement Methods. Photonics 2026, 13(1), 56. DOI: 10.3390/photonics13010056

[2] TU Darmstadt. Größe und Geschwindigkeit von Partikeln und Tropfen messen. ACHEMA Daily, Frankfurt am Main, Germany, 2012. TU Darmstadt Archive

[3] Schäfer, W. Time-Shift Technique for Particle Characterization in Sprays. PhD Thesis, Technische Universität Darmstadt, Darmstadt, Germany, 2012. Published as a book by epubli, Berlin, 2013, ISBN 978-3-8442-6708-2. Publisher Page

[4] Schäfer, W.; Tropea, C. The time-shift technique for measurement size of non-transparent spherical particles. In Proceedings of the International Conference on Optical Particle Characterization (OPC 2014), Tokyo, Japan, 10–14 March 2014; Proceedings of SPIE, Vol. 9232, 92320H. DOI: 10.1117/12.2063342

[5] Tropea, C.; Schaefer, W. Method and Device for Determining Characteristic Properties of a Transparent Particle. U.S. Patent Application US20170010197A1, 12 January 2017. US20170010197A1 – Google Patents

[6] Schaefer, W.; Li, L. Particle characterization by analyzing light scattering signals with a machine learning approach. Applied Optics 2024, 63(29), 7701–7707. DOI: 10.1364/AO.531346

[7] Schaefer, W. Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach. In Proceedings of THMT-25: Turbulence, Heat and Mass Transfer 11, Tokyo, Japan, 21–25 July 2025; Begell House: Danbury, CT, USA, 2025, p. 8. DOI: 10.1615/THMT-25.10

ai-quanton works with research organizations and industrial partners on new challenges in spray measurement, particle characterization, and process monitoring. If your application requires a new optical sensor, measurement concept, analysis method, or AI-assisted evaluation approach, we are interested in discussing possible research and development projects.

Learn more about our research, technologies, publications, measurement demonstrations, and current development projects throughout the ai-quanton website.

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