ASME

Journal of Computing and Information Science in Engineering

Zhinan 
Zhang, Ph.D.
Zhinan Zhang

Areas of Interest

DATA-DRIVEN ENGINEERING DESIGN AND APPLICATIONS
DIGITAL TWIN ENHANCED DESIGN AND ANALYSIS
MACHINE LEARNING AND TRIBOINFORMATICS FOR MACHINE HEALTH
Shanghai Jiao Tong University
Dr. Zhinan Zhang holds a full professorship in the School of Mechanical Engineering at Shanghai Jiao Tong University. His research interests include digital twin-enhanced design and analysis, triboinformatics, machine learning and emerging computational approach for tribology and machine health. He has published more than one hundred technical articles in journals, refereed conference proceedings, and edited books. He is a member of the China Education Association of Machinery Industry, the American Society of Mechanical Engineers (ASME), and the American Society of Engineering Education (ASEE). He serves as the associate editor of the ASME Journal of Computing and Information Science in Engineering and is on the Editorial Board of the Friction Journal. He serves as guest editor of the ASME Journal of Tribology and Friction. He has received several honors and awards for his contributions to engineering education. Representative examples include the Second Prize of the National Teaching Achievement Award, the Baosteel Excellent Teacher Award, the Shanghai Talent Award, and the Teaching Excellence Award of Shanghai Jiao Tong University. He has been awarded as a member of the Digital Twin International Advisory Committee. He has also received the best paper award in Friction Journal in 2019 and the reviewers’ favorite paper award in ICED11 and ICED13.

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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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