The so-called micrograph examination, part of metallography, is an important step in material analysis, because micrograph analysis is used to examine the microstructure of microsections of material samples in detail. Micrograph analysis is often used in metallurgy, materials science and quality control to get to the bottom of the internal structure of materials. One example of this is the macrosection of the weld seam, which is used to check the quality and integrity of the welded joint. This method of material analysis is very time-consuming, as thin sections must first be taken from the metal samples, which are then etched and finally analyzed under a microscope in order to examine phase fractions, inclusions, microstructures and other microscopic features. However, these parameters provide information about the quality of the material test. To make this process easier, artificial intelligence is increasingly being used for quality testing.
AI.SEE™ revolutionizes previous methods of material analysis through the use of artificial intelligence. Instead of manual processes, which take up a lot of time and resources, powerful machine learning algorithms enable fast and precise analysis of micrographs, which is particularly impressive due to the accuracy and objectivity of its results, e.g. in weld seam testing. AI.SEE™ automatically detects structures, layer thicknesses and inclusions, reducing human error while maximizing the accuracy of the quality inspection. Artificial intelligence can even process large quantities of micrographs in the shortest possible time, speeding up the analysis process and increasing efficiency. Thanks to the learning behavior of artificial intelligence, the evaluation of the micrograph analysis is not only reliable, but can also be fully automated. Artificial intelligence therefore takes micrograph inspection to a whole new level. Find out below in which areas artificial intelligence can provide you with concrete support.
Step 1:
Recognition & segmentation of materials (a,c,b)
Step 2:
Measurement of material thicknesses (d)
Step 3:
Pixel-precise localization of relevant measuring points (e)
Step 4:
Calculation of measuring distances and checking for tolerances (f)
AI.SEE™ is ideally suited for the analysis of microstructures, inclusions and phase fractions as well as the macrosection inspection of weld seams shown above, as the software can reliably automate metal samples for quality control, development and research as part of the micrograph inspection.
For the characterization of materials, surface mediation in various areas is a decisive factor for quality testing. Artificial intelligence identifies and measures the different phases or components on a ground surface - automatically and without manual control.
As part of quality control, it is important to evaluate the mechanical properties of materials. For this purpose, hardness profiles are created as part of the hardness test and their distribution is examined at the micro level.
AI.SEE™ can also automate the so-called calotte grinding process. Dome grinding is extremely important for the analysis of coatings, layers and surface structures and can be automated and carried out precisely using artificial intelligence.
AI.SEE™ has also already proven itself in the determination of grain sizes in metallurgical samples. This is because only automated grain size analysis can reliably provide a detailed insight into the structure of materials and is a decisive criterion for assessing quality.
Particle analysis is also an important aspect of micrograph testing. Here, particles in a material are identified, characterized and quantified. Automated particle analysis using artificial intelligence impresses with its precision and enables, among other things, the determination of particle concentration and their morphological characterization.
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