Day: November 15, 2023

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.

The Editor and Editorial Board of the Journal of Computing and Information Science in Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2023. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work.

This special issue includes eight articles describing fundamental and applied research on Extended Reality (XR) techniques, solutions, and challenges in the area of design and manufacturing. This issue aims to cover all aspects of XR technology, including technical advances in software/hardware, algorithms, ergonomic analysis, system implementation, and novel empirical studies, for product design and manufacturing processes.

This special issue is dedicated to promoting the dissemination of knowledge related to complex networks in engineering systems and design and highlighting the latest advances at the intersection of network science, graph theory, and engineering.

This special issue seeks to explore and compile the forefront of research and innovation in HRC within the context of Industry 5.0.

This special issue aims to explore and resolve issues related to effective engineering model representation for ML, the integration of neural networks and deep generative models into CAD/CAM, mathematical frameworks combining ML with CAD/CAM in geometry and topology, data interpretation, and physics-based learning.