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Journal of Computing and Information Science in Engineering

Machine Learning Applications in Manufacturing

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Guest Editors: Ying Liu (Cardiff University), Rahul Rai (University at Buffalo SUNY), Anurag Purwar (Stony Brook University), and Mahesh Mani (Allegheny Science & Technology)

Paper No: JCISE-20-1041 https://doi.org/10.1115/1.4046427

Published Online: March 3, 2020 Article history

Part I: J. Comput. Inf. Sci. Eng. April 2020, Volume 20, Issue 2

Part II: J. Comput. Inf. Sci. Eng. June 2020, Volume 20, Issue 3

Machine learning (ML) has recently become a power-engine transforming various manufacturing research and applications. In the era of Smart Manufacturing and I4.0, the abundance of smart sensors and industrial Internet of things has made manufacturing systems a data-rich environment. ML techniques play a significant role in uncovering fine-grained complex production patterns and offering timely decision support in a wide range of applications, to name a few, robotics and human–machine interaction, predictive maintenance, process optimization, task scheduling, quality improvement, and so on. While different ML techniques have been researched and deployed in manufacturing, many open challenges and questions still remain, from data understanding, data and knowledge representation, and data reasoning in ML to advanced topics such as predictive analytics, edge computing, and cybersecurity. Therefore, this special issue is dedicated to harvesting the latest research and development of ML in manufacturing. 

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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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