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Machine learning classification of surface fracture in ultra-precision diamond turning using CSI intensity map images

  • Published:2022-07 Issue: Volume:64 Page:657-667
  • ISSN:0278-6125
  • Container-title:Journal of Manufacturing Systems
  • language:en
  • Short-container-title:Journal of Manufacturing Systems

Author:

Nogueira Monica L.,Greis Noel P.,Shah Rachit,Davies Matthew A.,Sizemore Nicholas E.

Funder

University of North Carolina at Charlotte

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference44 articles.

1. Application of machine learning to the prediction of surface roughness in diamond machining;Sizemore;Procedia Manuf,2020

2. Spatial variations in stress and crystal quality in diamond turned ZnSe surfaces measured by Raman spectroscopy;Shojaee;Manuf Lett,2014

3. Review of several precision finishing processes for optics manufacturing;Ghosh;J Micro,2018

4. Atomistic configurational forces in crystalline fracture;Park;Forces Mech,2021

5. A combined finite-discrete element method in transient dynamics of fracturing solids;Munjiza;Eng Comput,1995

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1. Predictive Modeling for Mold Material Defects Using CNN-Random Forest Fusion;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

2. Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting;Science China Technological Sciences;2024-04-25

3. Intelligent Contour Error Compensation of Ultraprecision Machining Using Hybrid Mechanism-Data-Driven Model Assisted With IoT Framework;IEEE Transactions on Industrial Informatics;2024

4. Multi-Scale Sensing Network for WIFI Indoor Localization;2023 IEEE 23rd International Conference on Communication Technology (ICCT);2023-10-20

5. Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement;Electronics;2023-04-11
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