Image Evaluation in Hardness Testing with AI

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 impressions according to Vickers, Knoop, and Brinell, even on challenging surfaces.

The data-driven deep learning model consists of numerous hardness test impressions, 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 impressions, 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.

The fully automated impression 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 impressions 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.

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.

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.

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.