Jack L. Beuth

Mechanical Engineering · 2001–2020

A researcher in the Science corpus: 14 papers; trusts 11 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Mechanical Engineering — 150 citations into it; #406 of 8,300 by trust here, #559 by citations
• Automotive Engineering — 107 citations into it; #239 of 3,081 by trust here, #398 by citations
• Industrial and Manufacturing Engineering — 26 citations into it; #346 of 2,521 by trust here, #973 by citations
• Computational Mechanics — 25 citations into it; #4,783 of 10,001 by trust here, #1,325 by citations
• Materials Chemistry — 23 citations into it; #4,477 of 11,513 by trust here, #4,843 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Artificial Intelligence — 8 citations into it

Whom Jack L. Beuth trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. P. Kobryn
2. Marco Grasso
3. David G. Lowe
4. S. Semiatin
5. N. Klingbeil
6. K. Taminger
7. Yann LeCun

Who trusts Jack L. Beuth overall

1. Guo Dong Goh
2. T. DebRoy
3. T. Mukherjee
4. A. Pinkerton
5. Swee Leong Sing
6. Nikolas Hrabe
7. A. du Plessis
8. H. Wei
9. W. Y. Yeong
10. Luke Scime

Papers from Jack L. Beuth's perspective

Ordered by how strongly the researchers Jack L. Beuth trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Going deeper with convolutions
9. ImageNet Large Scale Visual Recognition Challenge
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Overview of Materials Qualification Needs for Metal Additive Manufacturing (2016) — 562 citations
• A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process (2018) — 332 citations
• Integrated control of solidification microstructure and melt pool dimensions in electron beam wire feed additive manufacturing of Ti-6Al-4V (2014) — 193 citations
• Evaluating the Effect of Processing Parameters on Porosity in Electron Beam Melted Ti-6Al-4V via Synchrotron X-ray Microtomography (2016) — 144 citations
• Defect structure process maps for laser powder bed fusion additive manufacturing (2020) — 335 citations
• Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process (2019) — 352 citations
• Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process (2019) — 162 citations
• Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm (2018) — 465 citations
• Thermal conductivity of metal powders for powder bed additive manufacturing (2018) — 199 citations
• Synchrotron-Based X-ray Microtomography Characterization of the Effect of Processing Variables on Porosity Formation in Laser Power-Bed Additive Manufacturing of Ti-6Al-4V (2017) — 323 citations
• Prediction of lack-of-fusion porosity for powder bed fusion (2017) — 622 citations
• Process Maps for Predicting Residual Stress and Melt Pool Size in the Laser-Based Fabrication of Thin-Walled Structures (2007) — 188 citations
• Residual stress-induced warping in direct metal solid freeform fabrication (2002) — 165 citations
• The role of process variables in laser-based direct metal solid freeform fabrication (2001) — 184 citations