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3D CNNs for Smart Manufacturing Voxel-Based Classification of Industrial Workpieces
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Author(s): Masakazu Hirono (Sanyo-Onoda City University, Japan), Fusaomi Nagata (Sanyo-Onoda City University, Japan), Maki K. Habib (The American University in Cairo, Japan), Keigo Watanabe (Okayama University, Japan), Hitoshi Nakamura (Research and Development Center, Gunma, Mitsubishi Pencil Co., Ltd., Japan)and Hisami Tamano (Research and Development Center, Gunma, Mitsubishi Pencil Co., Ltd., Japan)
Copyright: 2026
Pages: 18
Source title:
AI-Driven Smart Industrial Technologies
Source Author(s)/Editor(s): Maki K. Habib (The American University in Cairo, Egypt)
DOI: 10.4018/979-8-3693-7994-3.ch001
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Abstract
This chapter highlights the broader implications of adopting 3D CNNs in smart manufacturing. By automating the classification of complex workpieces, industries can achieve higher precision, reduce reliance on manual labor, and enhance production efficiency. Additionally, the integration of voxel-based classification systems into IoT-enabled smart factories is discussed is explored, presenting a pathway toward more intelligent and adaptive manufacturing ecosystems. This chapter also provides a practical guide to implementing 3D CNNs in industrial settings, Addressing both technical and real-world considerations. It contributes to the advancement of AI-driven smart technologies and serves as a valuable resource for researchers and practitioners in smart manufacturing.
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