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Journal of Computing and Information Science in Engineering

Special Issue: Machine Learning and Representation Issues in CAD/CAM

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Guest Editorial

J. Comput. Inf. Sci. Eng. Jan 2024, 24(1): 010301 (2 pages)

Machine learning (ML), a sub-field of artificial intelligence (AI), is profoundly reshaping various aspects of human life. Its application in engineering systems promises to address long-standing challenges, although it also introduces new questions. While the potential of ML is undeniable, integrating existing ML methods into computer-aided design and manufacturing (CAD/CAM) presents distinctive challenges. These challenges encompass representation, adaptation, and the development of novel ML techniques to enhance CAD/CAM systems for diverse design and manufacturing solutions.

Guest Editors

Anurag Purwar
Stony Brook University, USA
 
Kaushalkumar A. Desai
Indian Institute of Technology Jodhpur, India
 
Stephen Canfield
Tennessee Technological University, USA
 
Rahul Rai
Clemson University, USA
 
Zhenguo Nie
Tsinghua University, China

Review Article

Research Papers

J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011001. doi:https://doi.org/10.1115/1.4062233
 
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011002. doi:https://doi.org/10.1115/1.4062454
 
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011003. doi:https://doi.org/10.1115/1.4062661
 
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011004. doi:https://doi.org/10.1115/1.4062852
 
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011005. doi:https://doi.org/10.1115/1.4063275
 
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011006. doi:https://doi.org/10.1115/1.4063102
 
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011007. doi:https://doi.org/10.1115/1.4063226
 
 
 

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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.

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