Journal of Computing and Information Science in Engineering

Special Section on Symbiotic Human-AI Partnership for Next Generation Factories

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Volume 22, Issue 5 (October 2022)

As envisioned by Industry 4.0, the next generation of smart factories and warehouses will highly depend on the collaboration between human and artificial intelligence (AI). This symbiotic partnership can augment human capabilities by providing suggestions, assistance, and explanations as needed – or can utilize direct or indirect human feedbacks in a human-in-the-loop learning framework to enhance AI learning capabilities.

This Special Section aims to harvest the latest efforts in fundamental methodologies as well as their applications in human-AI partnership with specific applications for next-generation factories encompassing the design process to manufacturing, production, and inspection. 

Read Guest Editorial here:


Topic Areas

Potential topics in the context of next-generation factories include, but are not limited to:
• Human-AI partnership for supply chain and sustainable manufacturing systems
• Computational tools for human perception, cognitive assessment, and intention estimation
• AR/VR and novel interfaces for enhancing human-AI partnership
• Co-design of human-AI systems for manufacturing and automation
• System architecture for human-in-the-loop learning systems
• Evaluation methods and assessment of human-AI partnership
• Ethical consideration and financial impact of human-AI partnership on future manufacturing
• Digital twin in manufacturing with human-AI cooperation
• Human-AI partnership in robotics
• Human-AI partnership for workforce training/education
• Case studies and critical literature review


Special Section Editors

Ehsan T. Esfahani, PhD, Mechanical and Aerospace Engineering, University at Buffalo, USA,

Rahul Rai, PhD, Automotive Engineering, Clemson University, USA,

Ying Liu, PhD, Mechanical and Manufacturing Engineering, Cardiff University, UK,

Gaurav Ameta, PhD, Siemens, USA,

Chih-Hsing Chu, PhD, Industrial Engineering and Engineering Management, National Tsing Hua University,

Bin He, PhD, Shanghai University,


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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.