QATM QAI – AI-based indentation image analysis

Applications

For laboratories working with surfaces that are difficult to evaluate (etched microsections, rough castings, low-contrast coatings), where the classic algorithm requires manual correction.

Key features

• Higher image evaluation quality and recognition success rate
• Fewer manual checks – more automation
• The same indentation image always gives the same result (repeatability)
• Runs entirely locally on the PC, with no data exchange with QATM
• Model retraining only possible through QATM – stable, controlled behaviour

Models

QAI add-on for Qpix Control2

Technical data

• Methods: Vickers, Knoop, Brinell
• Integration: Qpix Control2 (replaces the existing image recognition algorithm)
• Operation: locally, without internet connection
• Retraining: QATM only

What we offer

• Selection of the instrument and configuration for your task, together with the manufacturer
• Trial measurements of your samples in the manufacturer’s laboratory
• Training, calibration and service organised through the manufacturer

Request a quote

Technical data according to the manufacturer: qatm.com, qatm.com