QpixControl2 — 集成先进AI功能，重新定义硬度测试 自动、准确地检测硬度测试印痕——即使是复杂表面
This image evaluation is used in all areas of hardness testing, generally increasing the recognition rate, finding indentations in an image, and the quality and accuracy of the evaluation and analysis.
AI-based image evaluation significantly improves the quality of hardness test indentation detection.
The QAI offers greater added value for rough, grinded and etched surfaces. Especially with difficult material surfaces or etched surfaces, the recognition rate could be increased enormously.
QAI image evaluation is fully integrated into the QpixControl2 operating software and replaces the current image recognition algorithm.
The use of QAI image recognition has also increased the repeatability and systematic deviation of the machine. The accuracy of the evaluation has a major influence on the relative repeatability of the machine.
Comparison between Classic evaluation and QAI evaluation
90 Hardness test points on a test block HV1 value 701 HV. The different evaluation modes are carried out on the same 90 indentations.
| Mean value | Range |
| 700,04 | 24,90 |
| Hardness min. | Hardness max. |
| 688,80 | 713,70 |
| Standard deviation | Results OK |
| 5,88 | 90 |
| Mean value | Range |
| 701,50 | 16,40 |
| Hardness min. | Hardness max. |
| 692,50 | 708,90 |
| Standard deviation | Results OK |
| 3,47 | 90 |
The AI and its image recognition runs exclusively locally on the PC and only within the QpixControl2 software, all data is offline and does not require internet access.
The AI model cannot develop and learn on its own; this function and work can only be performed by QATM, which ensures that only a certified QAI is used on the device. A hardness tester must work in accordance with the standards, therefore these results must be verified by us.
All data is stored locally on the PC and in the software, there is no data exchange with QATM. The QAI results are always the same.
100% offline solution
100% local data
No continuous development of the QAI on the machine
NO. The AI-based image recognition does not affect the optical system. The magnification, camera, and lenses remain unchanged. QAI analyzes the captured image and detects the hardness test indentation. The evaluation and measurement process follow the same principles as conventional hardness testing software.
NO. The relevant standards (DIN EN ISO, ASTM) specify requirements for sample preparation but do not define surface quality parameters such as roughness values (Ra/Rz). In general, the surface should be prepared appropriately for the Vickers hardness test, depending on the applied load. The indentation and its edges must be clearly visible.
Possibly, yes. QAI image evaluation can detect hardness indentations even on lower-quality surfaces. We recommend maintaining your current preparation process initially. However, step-by-step optimization is possible and should be validated accordingly.
Important note: The customer is responsible for defining and verifying their process. QATM can provide guidance and support.
YES. Technically and from a software perspective, direct hardness testing on etched surfaces is possible. QAI image evaluation can achieve very good detection rates even in these cases. However, standards recommend performing hardness tests on non-etched surfaces. The final responsibility for process validation lies with the customer.
NO. The AI and image recognition operate entirely locally on the PC within the QpixControl2 software. All data remains offline, and no internet access is required.
NO. The AI model cannot develop and learn itself independently. In the case that the QAI software cannot recognize hardness test impressions, there is the possibility to relearn the QAI by QATM.
