This study explores the diverse applications, challenges, and future prospects of employing
vision transformers in various material science domains, including biomaterials, ceramic …

Microstructure analysis is a crucial aspect of additive manufacturing (AM) processes, as it
offers valuable insights into material properties, defects, and quality of printed parts …

CS-UNet is a U-shaped image-segmentation algorithm with parallel CNN and Transformer
encoders. This algorithm leverages the relative strength of CNN and Transformers and …

Transfer learning improves the performance of deep learning models by initializing them
with parameters pre-trained on larger datasets. Intuitively, transfer learning is more effective …

A lens-free on-chip digital holographic microscope (LFOCDHM) is essential for a variety of
biomedical applications such as cell cycle assays, drug development, digital pathology, and …

Featured Application Image generation of steel microstructures, enhancing materials
research, quality control, and education by providing efficient and accurate visual …

The compressive strength of concrete is a crucial parameter in structural design, yet its
determination in a laboratory setting is both time-consuming and expensive. The prediction …

The research presented in the article indicates the process of building models based on
machine learning algorithms, linear regression, decision trees, ensemble learning, random …

The aim of this research was to develop a solution based on existing methods and tools that
would allow the automatic classification of selected images of cast iron microstructures. As …

Austenitic 347H stainless steel offers superior mechanical properties and corrosion
resistance required for extreme operating conditions such as high temperature. The change …