Matt Behnke

DIMACS REU 2020

References


  1. Abdi, H. and Williams, L.J. (2010), Principal component analysis. WIREs Comp Stat, 2: 433-459. doi:10.1002/wics.101

  2. Cheolhee Yoo, Daehyeon Han, Jungho Im, Benjamin Bechtel,Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 157, 2019, Pages 155-170, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2019.09.009.

  3. Heigel, J., P. Michaleris, and E.W. Reutzel, Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti–6Al–4V.Additive manufacturing, 2015. 5: p. 9-19.

  4. Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ArXiv, abs/1502.03167.

  5. Jafari, Roy & Khanzadeh, Mojtaba & Tian, Wenmeng & Smith, Brian & Bian, Linkan. (2019). From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing. Journal of Manufacturing Systems. 51. 10.1016/j.jmsy.2019.02.005.

  6. Khanzadeh, M., S. Chowdhury, M. Marufuzzaman, M.A. Tschopp, and L. Bian, Porosity prediction: Supervised-learning of thermal history for direct laser deposition.Journal of manufacturing systems, 2018.

  7. Khanzadeh, M., W. Tian, A. Yadollahi, H.R. Doude, M.A.Tschopp, and L. Bian, Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Additive Manufacturing, 2018.

  8. Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems. 25. 10.1145/3065386.

  9. Lei Zhang, Weisheng Dong, David Zhang, Guangming Shi,Two-stage image denoising by principal component analysis with local pixel grouping, Pattern Recognition, Volume 43, Issue 4, 2010, Pages 1531-1549, ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2009.09.023.

  10. Luke Scime, Jack Beuth,Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm, Additive Manufacturing, Volume 19, 2018, Pages 114-126

  11. Michael Schmidt, Marion Merklein, David Bourell, Dimitri Dimitrov, Tino Hausotte, Konrad Wegener, Ludger Overmeyer, Frank Vollertsen, Gideon N. Levy, Laser based additive manufacturing in industry and academia, CIRP Annals, Volume 66, Issue 2, 2017, Pages 561-583,

  12. Millard, K.; Richardson, M. On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping. Remote Sens. 2015, 7, 8489-8515.

  13. Mojtaba Khanzadeh, Sudipta Chowdhury, Mark A. Tschopp, Haley R. Doude,Mohammad Marufuzzaman & Linkan Bian (2019) In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes, IISE Transactions, 51:5, 437-455, DOI:10.1080/24725854.2017.1417656

  14. Mudrová, M., & Procházka, A. (2005). PRINCIPAL COMPONENT ANALYSIS IN IMAGE PROCESSING.

  15. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (January 2014), 1929–1958.

  16. Peijun Du, Alim Samat, Björn Waske, Sicong Liu, Zhenhong Li,Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 105, 2015, Pages 38-53, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2015.03.002.

  17. Ruder, S. (2016). An overview of gradient descent optimization algorithms. ArXiv, abs/1609.04747.

  18. Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.

  19. Wei Gao, Yunbo Zhang, Devarajan Ramanujan, Karthik Ramani, Yong Chen, Christopher B. Williams, Charlie C.L. Wang, Yung C. Shin, Song Zhang, Pablo D. Zavattieri,The status, challenges, and future of additive manufacturing in engineering, Computer-Aided Design, Volume 69, 2015, Pages 65-89, ISSN 0010-4485, https://doi.org/10.1016/j.cad.2015.04.001.

  20. Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li,Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives, Engineering, Volume 5, Issue 4, 2019, Pages 721-729, ISSN 2095-8099, https://doi.org/10.1016/j.eng.2019.04.012.

Acknowledgement of funding goes to the NSF through NSF grant CCF-1852215.

Thank you to the Rutgers DIMACS Team and NSF for the oppurtunity!