Paul Witherell

2.8k total citations · 1 hit paper
102 papers, 2.0k citations indexed

About

Paul Witherell is a scholar working on Automotive Engineering, Industrial and Manufacturing Engineering and Mechanical Engineering. According to data from OpenAlex, Paul Witherell has authored 102 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Automotive Engineering, 69 papers in Industrial and Manufacturing Engineering and 65 papers in Mechanical Engineering. Recurrent topics in Paul Witherell's work include Additive Manufacturing and 3D Printing Technologies (74 papers), Manufacturing Process and Optimization (64 papers) and Additive Manufacturing Materials and Processes (58 papers). Paul Witherell is often cited by papers focused on Additive Manufacturing and 3D Printing Technologies (74 papers), Manufacturing Process and Optimization (64 papers) and Additive Manufacturing Materials and Processes (58 papers). Paul Witherell collaborates with scholars based in United States, France and South Korea. Paul Witherell's co-authors include Yan Lu, Shaw C. Feng, Gaurav Ameta, David W. Rosen, Timothy W. Simpson, Hyunwoong Ko, Ian R. Grosse, Sundar Krishnamurty, Robert R. Lipman and Samyeon Kim and has published in prestigious journals such as Proceedings of the IEEE, IEEE Access and Additive manufacturing.

In The Last Decade

Paul Witherell

98 papers receiving 2.0k citations

Hit Papers

Research and application of machine learning for additive... 2022 2026 2023 2024 2022 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Research and application of machine learning for additive manufacturing
    2022 265 citations Jian Qin, Fu Hu et al. Additive manufacturing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Paul Witherell United States 25 1.3k 1.2k 1.2k 148 134 102 2.0k
Yan Lu United States 22 808 0.6× 682 0.6× 844 0.7× 85 0.6× 98 0.7× 65 1.8k
Qiang Huang United States 28 1.2k 1.0× 975 0.8× 1.4k 1.2× 60 0.4× 125 0.9× 133 2.3k
Ramy Harik United States 25 1.0k 0.8× 650 0.5× 1.3k 1.2× 124 0.8× 67 0.5× 108 2.7k
Yicha Zhang France 27 940 0.7× 1.1k 0.9× 931 0.8× 35 0.2× 85 0.6× 82 1.9k
Alexios Papacharalampopoulos Greece 25 1.0k 0.8× 594 0.5× 869 0.8× 27 0.2× 73 0.5× 91 2.1k
Jill Urbanic Canada 22 982 0.8× 521 0.4× 740 0.6× 37 0.3× 68 0.5× 121 1.7k
M. V. A. Raju Bahubalendruni India 28 695 0.6× 400 0.3× 1.1k 1.0× 64 0.4× 98 0.7× 82 2.1k
Kari Tammi Finland 30 289 0.2× 1.3k 1.1× 486 0.4× 108 0.7× 59 0.4× 136 2.8k
J. Balič Slovenia 25 1.2k 0.9× 302 0.2× 866 0.8× 280 1.9× 54 0.4× 98 2.2k
Guanghui Zhou China 30 678 0.5× 224 0.2× 1.6k 1.4× 108 0.7× 336 2.5× 106 2.8k

Countries citing papers authored by Paul Witherell

Since Specialization
Citations

This map shows the geographic impact of Paul Witherell's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Paul Witherell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Witherell more than expected).

Fields of papers citing papers by Paul Witherell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul Witherell. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Paul Witherell. The network helps show where Paul Witherell may publish in the future.

Co-authorship network of co-authors of Paul Witherell

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Witherell. A scholar is included among the top collaborators of Paul Witherell based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Paul Witherell. Paul Witherell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chen, Hongrui, et al.. (2024). MeltpoolGAN: Melt pool prediction from path-level thermal history. Additive manufacturing. 84. 104095–104095. 10 indexed citations
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Mahadevan, Sankaran, et al.. (2024). Active learning for adaptive surrogate model improvement in high-dimensional problems. Structural and Multidisciplinary Optimization. 67(7). 122–122. 2 indexed citations
4.
Mahadevan, Sankaran, et al.. (2024). Surrogate modeling of microstructure prediction in additive manufacturing. Computational Materials Science. 247. 113536–113536. 2 indexed citations
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Qin, Jian, Fu Hu, Ying Liu, et al.. (2022). Research and application of machine learning for additive manufacturing. Additive manufacturing. 52. 102691–102691. 265 indexed citations breakdown →
9.
Kumara, Soundar, Hui Yang, Timothy W. Simpson, et al.. (2022). Ontology Network-Based In-Situ Sensor Selection for Quality Management in Metal Additive Manufacturing. Journal of Computing and Information Science in Engineering. 22(6). 12 indexed citations
10.
Ko, Hyunwoong, et al.. (2021). Collaborative knowledge management to identify data analytics opportunities in additive manufacturing. Journal of Intelligent Manufacturing. 34(2). 541–564. 16 indexed citations
11.
Chen, Ruimin, Yan Lu, Paul Witherell, et al.. (2021). Ontology-Driven Learning of Bayesian Network for Causal Inference and Quality Assurance in Additive Manufacturing. IEEE Robotics and Automation Letters. 6(3). 6032–6038. 30 indexed citations
12.
Kumara, Soundar, et al.. (2021). Ontology-based Process Map for Metal Additive Manufacturing. Journal of Materials Engineering and Performance. 30(12). 8784–8797. 26 indexed citations
13.
Yang, Hui, Prahalada Rao, Timothy W. Simpson, et al.. (2020). Six-Sigma Quality Management of Additive Manufacturing. Proceedings of the IEEE. 109(4). 347–376. 50 indexed citations
14.
Yan, Wentao, Yan Lu, Zhuo Yang, et al.. (2020). Data-driven characterization of thermal models for powder-bed-fusion additive manufacturing. Additive manufacturing. 36. 101503–101503. 26 indexed citations
15.
Lu, Yan, Paul Witherell, & Albert Jones. (2020). Standard connections for IIoT empowered smart manufacturing. Manufacturing Letters. 26. 17–20. 18 indexed citations
16.
Ko, Hyunwoong, et al.. (2019). Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing. 648–654. 16 indexed citations
17.
Ko, Hyunwoong, et al.. (2019). A Framework for Identifying and Prioritizing Data Analytics opportunities in Additive Manufacturing. 3458–3467. 4 indexed citations
18.
Witherell, Paul, et al.. (2017). Toward a Digital Thread and Data Package for Metals-Additive Manufacturing. PubMed. 1(1). 75–100. 42 indexed citations
19.
Feng, Shaw C., Paul Witherell, Gaurav Ameta, & Duck Bong Kim. (2017). Activity model for homogenization of data sets in laser-based powder bed fusion. Rapid Prototyping Journal. 23(1). 137–148. 9 indexed citations
20.
Ameta, Gaurav, Paul Witherell, Shawn P. Moylan, & Robert R. Lipman. (2015). Tolerance Specification and Related Issues for Additively Manufactured Products. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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