Part 1: Fields of application and potential
The rapid development of computing power has made computers an indispensable part of modern society. Today, computers process huge amounts of data that help us to better understand complex information and gain new insights. This technological progress has paved the way for artificial intelligence (AI) – an innovative tool that has already revolutionized many fields and is now also changing materials science and engineering.
To better understand the benefits of AI for materials science, it is helpful to look at the evolution of scientific paradigms. Starting with empirical approaches, through the introduction of mathematical models and computer-aided simulations, we are now in the fourth paradigm of data-driven science. This paradigm uses AI and machine learning (ML) to recognize hidden patterns in large data sets and thereby generate new knowledge.
In this article, we present three central application areas of AI in materials science:
- microstructure analysis
The microstructure of a material – its microscopic structure – is decisive for its properties. Extracting information from the microstructure is therefore a fundamental task both for the quality assurance of existing materials and for the development of new materials.
Traditionally, manual or outdated methods are still often used here, which are subjective and not very reproducible.
AI-based microstructure analysis offers major advantages: it automates time-consuming tasks, increases the objectivity and reliability of the results and enables more precise quantification of microstructures. In this way, AI can raise microstructure analysis to a new level of quality and efficiency.
- property prediction and process optimization
AI-supported models make it possible to process large amounts of data from production processes and uncover correlations between process parameters, microstructure and material properties. These correlations form the basis for targeted process optimization and enable the prediction of product properties during production.
- anomaly detection and predictive maintenance
AI detects anomalies in production processes by identifying unusual patterns and deviations in production data. Using techniques such as unsupervised learning and clustering, outliers in sensor data, machine performance and production quality can be detected and potential problems can be rectified at an early stage. This predictive approach enables timely adjustments, minimizes defects, reduces downtime and improves overall production efficiency.
In our next article, we will present further fields of application for AI in materials technology and shed light on the amount of data required for these data-driven methods and the role our expert knowledge plays in data generation and analysis.
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