Journal of Computing and Information Science in Engineering

SPECIAL SECTION: Data Wrangling to Support Research on Engineering Design and Manufacturing

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December 2022, Volume 22 – Issue 6

The digitalization of manufacturing and the technologies associated with Industry 4.0 has led to an explosion in unstructured data across the entire product lifecycle, including engineering design and manufacturing activities, which are embodied in the emerging “digital thread” and corresponding “digital twin” of the product. These technologies expose rich information that can be used to achieve data-driven (re)design of products and engineering, support continuous improvement of manufacturing operations, and enhance product development practices. However, challenges persist across the entire product lifecycle due to the massive scale at which this data is generated and shared (e.g., some researchers have reportedly resorted to the inelegant solution of mailing hard drives). Significant challenges also arise due to the format, variety, and content of the data as well, limiting its broader use in engineering design and manufacturing research. 

This special issue aims to capture contemporary perspectives on both the challenges and opportunities regarding the generation, collection, curation, storage, transmission, and transformation of engineering design and manufacturing data in digital databases and repositories.  Topics of interest include, but are not limited to:

  • Methods for data storage, management, and curation of product lifecycle data
  • Repository-based exploration of design and manufacturing data
  • Translation and transmission techniques for facilitating scalable data-driven pipelines
  • Automated data/model generation for engineering workflows (e.g., virtual scenes and data-driven decision-making)
  • Opportunities of standards development for data management in engineering
  • Data representations and data schemas to enable the digital thread

Papers Published

J. Comput. Inf. Sci. Eng. December 2022, 22(6): 060901. doi:

J. Comput. Inf. Sci. Eng. December 2022, 22(6): 060902. doi:
J. Comput. Inf. Sci. Eng. December 2022, 22(6): 060903. doi:
J. Comput. Inf. Sci. Eng. December 2022, 22(6): 060904. doi:
J. Comput. Inf. Sci. Eng. December 2022, 22(6): 060905. doi:


Guest Editors

  • Christopher McComb, Carnegie Mellon University,
  • William Bernstein, Air Force Research Laboratory,
  • Vincenzo Ferrero, National Institute of Standards and Technology,
  • Timothy W. Simpson, The Pennsylvania State University,
  • Nicholas A. Meisel, The Pennsylvania State University,
  • Binil Starly, North Carolina State University, bstarly@ncsu.ed


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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.


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.