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Journal of Computing and Information Science in Engineering

Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

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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.

This special issue will solicit articles that conduct research on addressing the challenges of geometric data analytics for advanced manufacturing. We seek papers that develop new geometric data mining tools for design optimization, product customization, quality control, process monitoring, simulation, robot- and/or human-in-the-loop manufacturing, additive manufacturing, and other related research areas. This special issue is expected to have a significant impact by enhancing multiple facets in manufacturing research.

Topic Areas

THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

  •  In-situ and ex-situ geometric data analytics for process monitoring and quality control of 3D printing, bioprinting and additive manufacturing
  • Design optimization and shape reconstruction
  • Industrial Internet of Things (IIoT) sensor network modeling and optimization
  • Advanced sensing technology for geometric data acquisition, such as new 3D scanning instrumentation, Lidar, and stereo vision technology
  • Geospatial information analysis for manufacturing systems and supply chain networks
  • Robots/Cobots path planning and control, and machine toolpath planning
  • Statistical methods and machine learning approaches for point cloud denoising, completion, segmentation, clustering, and classification
  • Mesh and networked data modeling and analysis for manufacturing applications
  • XR (e.g., virtual reality and augmented reality) technologies for immersive visualization, interactive analytics, and digital twins of advanced manufacturing
  • Extending large language models (LLM) for industrial geometric data analysis

Special Issue Publication Dates

Paper submission deadline: December 31, 2024
Initial review completed: March 1, 2025
Publication date: September 2025

Submission Instructions

Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here

Once at the Paper Submittal page, select the Journal of Computing and Information Science in Engineering, and then select the Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing.

Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.

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Guest Editors

Dr. Bianca Maria Colosimo, Politecnico di Milano, Italy (biancamaria.colosimo@polimi.it)

Dr. Jonathan Corney, University of Edinburgh, UK (j.r.corney@ed.ac.uk)

Dr. Chen Kan, The University of Texas at Arlington, USA (chen.kan@uta.edu)

Dr. Gregory W. Vogl, National Institute of Standards and Technology (NIST), USA (gregory.vogl@nist.gov)

Dr. Yinan Wang, Rensselaer Polytechnic Institute, USA (wangy88@rpi.edu)

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Announcements

Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

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.

Announcements

2023 Reviewer’s Recognition

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.

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