QATM – 40 Years of Experience, One Mission: Quality in Every Detail

With over 40 years of expertise, QATM is a global leader in material testing. The innovative QAI image evaluation in the Qpix2 software transforms hardness testing through fully automated detection and analysis of indentations—even on challenging surfaces. Discover how AI boosts efficiency and shapes the future of quality assurance: Learn more

As a global leader in materialography and hardness testing, QATM combines decades of expertise with cutting-edge innovation – most recently through its advanced Qpix2 software featuring AI-powered image analysis (QAI). With over 40 years of expertise, the company develops innovative solutions for metallographic sample preparation and analysis – from surface processing to microstructure examination. The product portfolio includes high-quality machines, consumables, software, and complete laboratory solutions. As a quality and technology leader, QATM stands for reliability, precision, and customized solutions across a wide range of material classes

QAI Revolutionary Image Evaluation in Hardness Testing Sets New Standards with AI Technology 

The Qpix2 software from QATM revolutionizes hardness testing through the use of cutting-edge AI technology, marketed under the name QAI. This solution offers fully automated detection and evaluation of hardness test indentations according to Vickers, Knoop, and Brinell, even on challenging surfaces.

The data-driven deep learning model consists of numerous hardness test indentations, filled and verified by specialists and experts from the hardness testing field at QATM. A key highlight is the direct integration into the existing Qpix2 hardness testing software. The AI-supported detection QAI enables precise and rapid identification of hardness test indentations, making manual interventions unnecessary. This results in significant efficiency gains and paves the way for innovations in hardness testing. QAI provides unique accuracy and hit rates, giving users a decisive competitive edge.

Fully Automated Detection and Evaluation of Hardness Test Indentations

The fully automated indentation detection of the QAI software also works with low contrasts, difficult and etched surfaces, which are essential for quality inspection of weld seams. This includes various metallic materials and surface treatments used in the daily operations of hardness testing. The software can accurately recognize and evaluate hardness test indentations on polished, ground, and etched surfaces.

The Qpix2 software with directly integrated QAI from QATM sets new standards in hardness testing, offering users unprecedented automation and efficiency. With its unique accuracy and hit rates, it revolutionizes hardness testing and gives users a decisive competitive advantage. The future of hardness testing belongs to pioneers who use the QAI software from QATM to optimize their processes and drive innovation.

Try it with your own images and see if we can detect your hardness impressions too! Experience our revolutionary QAI solution online. You’ll be impressed!

Precision and Efficiency through AI-Powered Image Analysis

This fully integrated AI-supported detection QAI can also be executed on existing devices to sustainably support our customers and make a significant contribution to the quality assurance of critical components, such as the testing of railway wheels, aircraft turbine blades, or gears in passenger transport.

Future-Proof Quality Assurance for Demanding Applications

This AI solution is a significant step in taking quality assurance in hardness testing to the next level. The system's unique accuracy and hit rates also increase the comparability of measurement systems, reducing errors and failures.

To be prepared for the future, the AI-supported image recognition can be continuously optimized and specifically trained. This allows the QAI solution to be adapted to new requirements and to test and qualify new materials from the industry.

Continuous Development and Adaptability for Industry Needs

There are no obstacles to further developing the system, which can be widely used and adapted, especially in the fields of hardness testing and metallography. The question is not whether to use AI-supported systems in image recognition, but when and how.