Reliable and automated: AI-based material testing with AI.SEE™
In materials science, there is an infinite variety of materials, each with its own unique properties and specific requirements for analysis. With AI.SEE™, we offer an AI-based solution designed to overcome the challenges of manual microscopic material analysis. From particle and fibre testing to grain analysis and Micrograph inspection In metallography, AI-supported analyses deliver highly precise results. Our deep learning technology integrates quickly and easily into existing systems and takes the precision and speed of your analyses to a new level.
AI testing in material analysis
Precisely identifying and analysing a wide variety of materials is often time-consuming and complex. Our AI-supported material analysis offers a fast, precise and efficient solution. Utilise the power of artificial intelligence to overcome the challenges of modern materials science and gain a competitive advantage.
Particle analysis
The precise characterisation of particles in different materials and states of aggregation, from solids to liquids and gases, can be automated with AI.SEE™.
Fibre testing
Fibre testing with AI enables the automatic detection and characterisation of fibres in materials, checking for properties such as fibre length, diameter, distribution and orientation.
Multiphase testing
AI.SEE™ learns to identify and classify different phases within a metal alloy. This includes the recognition of distinct colour and texture differences in microscopic images that correspond to different phases.
Applications for metallography
Grain size determination, micrograph testing or porosity analysis - The Automated image analysis with AI delivers precise, fast and reliable results in metallography that go far beyond traditional methods. Here are some of the most important use cases of AI.SEE™ for testing metals.
Grain analysis
Our AI algorithms can be trained to automatically identify grains in a microscopic image and measure their size. This includes recognising grain boundaries and calculating the average grain size.
Micrograph inspection
AI.SEE™ can recognise specific microstructures such as inclusions, pores, cracks and other defects as well as geometric structures in micrographs and thus check the quality of weld seams, among other things. Find out more about micrograph testing.
Characteristics
Analysing the composition and structure of cast iron, for example, provides information on the material composition as well as the shape and distribution of individual components.
Frequently asked questions
- What is AI.SEE™?
AI.SEE™ is an AI-based software solution that is suitable for automated image analysis and quality control in laboratories. It uses advanced deep learning to precisely analyse microscope images.
- How is AI.SEE™ integrated into existing laboratory systems?
AI.SEE™ can be seamlessly linked to existing microscope systems via retrofit integration. Digital microscopes are integrated without additional hardware, while analogue microscopes require a camera adapter for image acquisition. The microscope serves as an imaging system that sends images to the AI.SEE™ software for automated AI analysis. The images and results can be viewed via a connected tablet or computer. The integration does not require extensive retrofitting or high investment and is designed to be user-friendly.
- What advantages does AI.SEE™ offer?
AI.SEE™ uses AI-based processes to increase the accuracy and efficiency of image analysis, reduce human error and significantly speed up the analysis process. Unlike conventional industrial image processing, AI.SEE™ is able to recognise even the most complex features despite noisy backgrounds or low contrast. Not only is it easy to process large volumes of data at high speed, but the solution also learns with every image analysed.
- What are the requirements for AI integration?
The only requirement is the ability to take images of the desired slides and features that can be used to train the AI model.
- What is AI training?
In AI training, an artificial intelligence (AI) is trained using large amounts of data - in this case image data - to recognise and interpret specific patterns and features. To analyse images effectively, the AI must be trained with a large number of images representing different scenarios and conditions. This enables the AI to achieve accuracy and reliability in automated analysis and efficiently perform complex tasks such as quality control in the laboratory.