ASME

Journal of Computing and Information Science in Engineering

Matthew I.  
Campbell, Ph.D
CAMPBELL

Areas of Interest

ARTIFICIAL INTELLIGENCE METHODS FOR COMPUTATIONAL DESIGN SYNTHESIS
MANUFACTURING PLANNING
Oregon State University, USA
Dr. Matt Campbell is a mechanical engineering professor with research focusing on automating difficult or tedious engineering design tasks. For over 20 years, he has focused on methods that independently create solutions for typical mechanical engineering design problems like gear trains, sheet metal, planar mechanisms, and planning for manufacturing, assembly and disassembly. In 2020, he was named an ASME fellow for his achievements in machine design, design theory, artificial intelligence, graph theory and numerical optimization. Prior to his current position within the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University, he was a William J. Murray Fellow at the Cockrell School of Engineering at The University of Texas at Austin, a Hans Fischer Senior Fellow at the Technical University of Munich, and a 2005 NSF CAREER awardee. He has over a hundred published articles and has been acknowledged with best paper awards at conferences by the ASME, ASEE, and the Design Society. He received his PhD from Carnegie Mellon University in 2000 with honors and membership in Phi Kappa Phi and Pi Tau Sigma.

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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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