Intelligent material analysis

Intelligent material analysis

Automated recognition of material properties with artificial intelligence

There are a large number of materials that need to be precisely identified during an analysis. Each material has its own properties and places special demands on the material analysis. This can take a lot of time, especially when it comes to complex materials that have to be analyzed in several steps. The solution: Intelligent material analysis – a material analysis using artificial intelligence (AI).

What is intelligent material analysis?

Intelligent material analysis is a method for automatically recognizing and classifying material properties and determining the quality and condition of the material. Technologies such as machine learning, image processing and optical sensor technology are combined to achieve this. Intelligent material analysis can be used in many industries, such as electronics, aerospace, medical technology, mechanical engineering and new energy vehicles. The analysis is used to monitor the properties of materials and products, to ensure material quality and safety and to improve the efficiency of processes.

Overview of possible material analyses

Image of a grain size analysis

Grain size analysis

Grain size analysis is used to analyze the size and distribution of material grains in order to investigate and evaluate the direct relationship between the material properties.

Image of a porosity analysis.

Porosity analysis

In a porosity analysis, material properties such as hardness, strength and failure strain are evaluated precisely and fully automatically in the image through the distribution of the pores in a microscopic image.

Image of a particle analysis.

Particle analysis

Particle analysis is a method used to determine the size, shape, number and distribution of particles in a sample. This analysis is used to monitor and control the quality of products and processes.

Image of a cast iron analysis.

Cast iron analysis

Cast iron analysis is used to determine the crystallographic composition and microscopic structure of cast iron. It can be used to characterize the properties of castings such as strength, hardness, shrinkage and abrasion and to monitor and optimize the quality of cast products.

Image of a multiphase analysis.

Multiphase analysis

The multiphase analysis is a phase calculation for the distribution in the sample image. The distribution and interaction of different phases in materials such as alloys, ceramics, polymer and metal-matrix composites can thus be determined.

Image of a coating thickness analysis.

Coating thickness analysis

A coating thickness analysis is a method for determining the thickness of a coating on a surface. It makes it possible to measure the thickness and homogeneity of coatings and ensure that they meet the specified requirements.

Award-winning user interface for your analyses

With AI, material analyses can be carried out faster, more efficiently and down to the smallest detail. Let us show you how artificial intelligence can provide an automated solution for your material analysis requirements. With ZEISS ZEN core you can take your material analyses to the next, intelligent level. To make the software easily accessible for every user, ZEISS has developed a special user interface that has won the German Design Award 2022. See ZEISS ZEN core for yourself and use artificial intelligence to improve your processes.

Your benefits with intelligent material analysis with the ZEISS ZEN core AI software

  • Work based on workflows and standards

    For grain size determination, directional series comparison, multiphase analysis and layer thickness including the classification of graphite particles

  • Intuitive user interface

    Results with just a few clicks

  • Integration of AI models

    Included in the standard scope of the software

  • Analysis of all common image formats

    Analysis of all established image formats such as JPEG, TIFF, PNG and many Bio-Formats and providers: IMAGIC IMS, dhs, Leica, Olympus, Nikon, FEI, Hitachi, JEOL, Keyence and many more

ZEN core material analyses in detail

  • Welded copper connection with visible grains

    Welded copper connection with visible grains

  • Automatic grain size analysis of an SEM image with histogram and mean grain size number.

    Automatic grain size analysis of an SEM image with histogram and mean grain size number.

What is a grain size analysis?

Grain size analysis is about determining the grain size and overall distribution of the grains as simply as possible. It can be difficult for users to determine the particle size distribution accurately and reliably for small particle sizes or samples with a broad size distribution. Therefore: The better the boundaries and grains are recognizable, the easier the analysis. To achieve the best results even under difficult conditions and with complex samples, intelligent material analysis can be the solution. A grain size analysis with AI requires less interactive rework than a manual analysis. Manual analyses take considerably longer and often require reworking. Thanks to deep learning, the AI of the ZEISS ZEN core software can be trained specifically for your grain size analysis requirements. This enables you to achieve reliable results in the shortest possible time, optimally tailored to your processes.

Routine inspection of additively manufactured aluminum-silicon sample
Routine inspection of additively manufactured aluminum-silicon sample
Routine inspection of additively manufactured aluminum-silicon sample

What makes multiphase/porosity analysis so special?

The special thing about multiphase analysis is that it enables the phase distribution of materials with several phases to be determined. A phase describes a pronounced and visible structure of a material. This allows the distribution and interaction of the different phases in the material to be determined and the effects on the properties of the material to be recognized. Multiphase analysis is a tried and tested method, especially when it comes to checking material quality and the further development and improvement of materials. It also provides a deeper insight into the properties and performance of materials, which is important for the development of new products and processes in many industries.

Material properties such as hardness, strength and failure strain are influenced by the distribution and orientation of the phases. A precise and fully automatic analysis of the sample structures according to size, shape or orientation can be visualized using multiphase analysis. Information on the porosity of additively manufactured materials or the percentage area distribution of inclusions/voids can thus be obtained and displayed. Precise segmentation of the phases with very small differences can be implemented using the artificial intelligence of ZEISS ZEN core.

  • AI-supported powder characterization of aluminium powder AlSi10Mg
    AI-supported powder characterization of aluminium powder AlSi10Mg
    AI-supported powder characterization of aluminium powder AlSi10Mg

    What is particle analysis and what is it used for?

    Particle analysis refers to the measurement and characterization of particles in a specific material or medium. Particles can come in different sizes and shapes and can consist of a variety of materials such as solids, liquids or gases.

    Particle analysis is used in many fields such as chemistry, materials science, biology and environmental science to improve the understanding of particle behavior and properties.

    ZEN core supports you in your analysis with machine learning tools and AI-supported object classification of particles, saving you tedious manual rework.

Automatic cast iron analysis
Automatic cast iron analysis
Automatic cast iron analysis

How does the cast iron analysis work?

Cast iron is often used in the manufacture of precision products, e.g. in the production of safety-relevant components in mechanical engineering. The size, shape and distribution of graphite and the ferrite-pearlite ratio are analyzed. The spheroidal and lamellar graphite particles in cast iron depend on the process parameters and the composition of the material. They are found in various forms and distributions and can therefore have a major influence on the mechanical properties of the material. You can analyze the shape and size of these particles with ZEISS ZEN core in a workflow-based and simple material analysis.

  • Li-ion battery: Layer thickness measurement of the copper conductor foil of an anode
    Li-ion battery: Layer thickness measurement of the copper conductor foil of an anode
    Li-ion battery: Layer thickness measurement of the copper conductor foil of an anode

    Are even the smallest layers recognizable and measurable?

    Usually, a cross-section of a sample is analyzed using segmentation or a neural network (deep learning). The ZEISS ZEN core software can evaluate information that is visible to the human eye. The slightest differences in color, shape and size can be distinguished with an intelligent material analysis using algorithms from the ZEN tool kit for artificial intelligence. This enables reproducible and semi-automatic determination of the layer thicknesses in just a few steps, which can also be interactive if required.

Fragen zu ZEN core

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Try out ZEN core

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