Artificial Intelligence and Machine Learning

The future of automation in data analysis

Artificial Intelligence (AI) and machine learning are groundbreaking technologies transforming the way we interact with and use data. AI refers to the broader concept of creating machines or systems that can perform tasks requiring human intelligence. Machine learning, on the other hand, is a subset of AI focusing on the development of algorithms and models that enable computers to learn patterns and make predictions from data without explicit programming.

Naturally, AI and Machine Learning are broadly used in GeoDict to aid our customers in their material development tasks.

AI-Features in GeoDict

Segmentation of gray-value images

Artificial Intelligence - particularly deep learning techniques - has proven to be highly effective in image segmentation tasks. This technology assists in transforming 3D gray-value images, obtained from FIB-SEM and micro-CT, into digital representations of materials. Within GeoDict, this capability is implemented in the ImportGeo-Vol module. Essentially, AI serves as a powerful tool in accurately defining the material phase associated with each voxel, contributing to the precise characterization of complex structures within the images.

Filtering of gray-value images

Machine Learning is used also to enhance image features and to improve the image quality of 3D gray-value images in the ImportGeo-Vol module of GeoDict. This capability finds application in scenarios where high-quality results are desired, but are constrained by factors like short scan times or initially low-quality images. Additionally, super-resolution techniques are used to enhance image detail and clarity beyond the inherent resolution limitations.


Fiber identification

Distinguishing between the fiber material and the pore space is possible when analyzing segmented 3D images featuring a fibrous or nonwoven material . However, directly assigning a solid voxel to a specific fiber presents challenges for conventional algorithms, such as those based on watershed methods. In this scenario, the pre-trained neural networks in GeoDict offer precise single fiber identification and are the foundation for highly accurate computations of fiber diameter, orientation, curvature, and length. The single fiber identification based on machine learning is found in the FiberFind module.

Binder identification

In a gray-value image, it is a challenge to distinguish between fibers and binders in a gas diffusion layer (GDL) of a fuel cell or between active material and binder in a battery electrode microstructure. GeoDict comes equipped with pre-trained neural networks designed to accurately identify the binder phase within 3D images. This enables the creation of a precise digital material representation and facilitates the incorporation of distinct properties of individual constituent materials. Consequently, this approach allows for accurate microstructure-based simulations. These trained neural networks are available in the FiberFind and GrainFind modules of GeoDict.

Train your own Neural Network

GeoDict comes equipped with powerful pre-trained Neural Networks designed for specific applications.

With GeoDict-AI, we offer an user-friendly solution for our customers to train their own Neural Networks. The networks are finely tuned to tackle diverse challenges in the analysis of 3D images. For example, to identify contact areas between individual grains in materials such as rocks or even battery electrodes.

Additionally, the capabilities of GeoDict in microstructure generation serve as an ideal solution to create virtually limitless amounts of training data for machine learning and, in this way, advance digital material development.

Training custom neural networks for object detection using GeoDict-AI

Please note that after activating the video, data will be transmitted to YouTube. 
More Information