AI-assisted materials analysis

Definition: AI-assisted materials analysis refers to the use of machine learning and artificial intelligence methods for the automated evaluation of materials data. This includes image analysis of microstructures, spectral data or process parameters. The aim is to identify patterns, anomalies and correlations with mechanical properties.

Practical relevance: Applications include automated microstructure classification (e.g. SEM/EBSD images), prediction of material properties, early failure detection and optimisation of additive manufacturing processes. The prerequisites are validated datasets, defined training models and transparent validation metrics. Insufficient data quality can lead to erroneous forecasts.

Decision-making perspectives:

• Technical decision-makers: Use of data-based models for process monitoring and quality forecasting.
• Purchasing/project management: Assessment of software solutions with regard to validatability and integration capability.
• Science: Development of explainable models (Explainable AI) and statistical validation.
• Insurance/law: Traceability of algorithmic decisions and documentation of training data.

Typical testing or verification methods: Image classification using neural networks, regression models, validation by reference tests and statistical metrics (e.g. accuracy, RMSE).

FAQ:

• Can AI replace classical materials testing?
• No, AI complements existing testing methods but does not replace physical validation through standard-compliant tests.