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| Pictures: ZwickRoell |
Mechanical materials testing is one of the most fundamental methods used in quality assurance—whether in the development of new materials, in production or in the laboratory. But what happens when artificial intelligence (AI) meets this established field? Can AI algorithms evaluate tensile tests, detect anomalies or intelligently control test sequences or even replace them?
The short answer: Yes, and in some cases, it is already in place. But how exactly does it work? And what advantages - but also risks - does the use of AI entail?
What can AI do in materials testing?
AI is more than just a buzzword. In practice, it can help to analyze huge amounts of data generated during materials testing more quickly, identify patterns and deviations, and make processes more efficient. This is particularly exciting in an environment that is becoming increasingly digital and automated - keyword Industry 4.0.
Here are five key areas where AI can already make a real difference in mechanical material testing today:
1. Automated evaluation of tests
How long does it take for a laboratory employee to analyze a stress-strain diagram and interpret characteristic values such as tensile strength? Especially when several samples are involved in a test series. With AI, this can often be done in seconds. Algorithms recognize the relevant points on the curve or in the results tables - regardless of whether it is a metal tensile test according to ISO 6892-1 or ASTM E8 or a completely different standard. This saves time and reduces subjective errors or can indicate which results can be looked at again in detail by experts.
In tests where optical analysis plays a key role, AI can also speed up evaluation and prevent errors. In hardness testing, for example, AI can provide efficient and reliable support in the recognition and interpretation of impressions.
2. Anomaly detection & predictive quality
What if you could detect material defects before they even became a problem? AI recognizes deviations and patterns in test data that escape the human eye. This enables predictive quality assurance - for example, by detecting batch deviations or faulty samples at an early stage.
It is important that all test data is stored centrally. The testXpert Analytics analysis platform provides such centralized access to all test and machine data.
3. Intelligent test planning & adaptive control
How can the inspection strategy for a new component geometry be optimized? AI can learn from previous tests and automatically suggest suitable test parameters - such as force ranges, speeds or temperature profiles. In combination with adaptive systems, the test even adapts to the material behavior in real time.
It should be noted here that adaptive control is not provided for or permitted in all test standards. However, if this is the case, the testing process can be accelerated and any failed tests can be significantly minimized.
4. Making robotics & automation smarter
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AI and robotics - a dream team? In modern testing facilities, for example with robotic systems or sample handling systems, AI takes over intelligent control: It detects samples using image processing, sorts them automatically and analyzes their behavior during the test. This leads to higher throughput and more stable processes.
It is important that the testing system has the corresponding options for integrating camera systems or other sensors into the testing process. On the one hand, this requires modern measurement and control electronics, and on the other, the testing software must have appropriate interfaces.
5. Digital twins & simulation-based tests
Is it possible to carry out a test without actually doing it? AI-supported digital twins can be used to simulate real material properties and compare them with real test results. This makes it possible to perform parts of the mechanical test virtually - particularly exciting in the case of cost-intensive or difficult-to-access components.
Completely foregoing tests with real application conditions is usually not permitted in safety-critical areas. A combination is more likely to be used: Simulation + targeted real test. It is important that the digital twin is “fed” with real test results, which are checked regularly so that it remains reliable.
