Dear Colleagues,

Materials research and development as well as manufacturing processes are becoming more and more complex. Shorter product lifecycles and increasing customer requirements demand new and faster solutions. To support this, data-driven methods can help to discover new ways and efficient processes in different areas: creating new materials, visualizing processes, recognizing patterns, predicting material properties, or supporting decision-making. The application of data-driven methods such as machine learning for regression and classification play a crucial role in taking advantage of these potentials. Based on certain challenges in material sciences and manufacturing, this Special Issue shall address the development and application of data-driven methods to contribute to a solution. Understanding the field of application and its data preparation, data modeling, model evaluation, and especially its deployment, are important steps to make efficient use of data-driven methods. Hence, with this Special Issue we aim to increase the visibility of significant application-oriented research devoted to data-driven methods for various objectives in material science and manufacturing processes.

We are particularly interested in contributions that focus on topics including (but not limited to):

- Applications of machine learning/AI for material sciences and manufacturing;

- Innovative characterization methods, e.g., for material fingerprinting;

- Sensor concepts for data-driven methods in smart manufacturing;

- Intelligent process management;

- (Big) data preparation, algorithms, modeling, and evaluation approaches;

- Deployment of data-driven methods in applications;

- Frameworks and concepts for the application of data-driven methods;

- Data analytics, data prediction, data classification;

- (Big) data in metrology and quality assurance.

Prof. Dr. Jürgen Hartmann
Prof. Dr. Jan Schmitt
Guest Editors

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