Ashley D. Spear

2.0k total citations
52 papers, 1.4k citations indexed

About

Ashley D. Spear is a scholar working on Mechanical Engineering, Mechanics of Materials and Automotive Engineering. According to data from OpenAlex, Ashley D. Spear has authored 52 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Mechanical Engineering, 19 papers in Mechanics of Materials and 13 papers in Automotive Engineering. Recurrent topics in Ashley D. Spear's work include Additive Manufacturing Materials and Processes (17 papers), Fatigue and fracture mechanics (15 papers) and Additive Manufacturing and 3D Printing Technologies (13 papers). Ashley D. Spear is often cited by papers focused on Additive Manufacturing Materials and Processes (17 papers), Fatigue and fracture mechanics (15 papers) and Additive Manufacturing and 3D Printing Technologies (13 papers). Ashley D. Spear collaborates with scholars based in United States, Germany and Spain. Ashley D. Spear's co-authors include Nadia Kouraytem, Wenda Tan, Xuxiao Li, Aowabin Rahman, Anthony D. Rollett, Jake T. Benzing, Nikolas Hrabe, Anthony R. Ingraffea, Gregory G. Deierlein and Judith Mitrani‐Reiser and has published in prestigious journals such as PLoS ONE, Acta Materialia and Carbon.

In The Last Decade

Ashley D. Spear

51 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ashley D. Spear United States 20 966 448 327 280 245 52 1.4k
Jiaqiang Li China 28 1.4k 1.4× 651 1.5× 659 2.0× 195 0.7× 111 0.5× 113 2.0k
Bradley Howell Jared United States 21 1.2k 1.3× 761 1.7× 213 0.7× 179 0.6× 120 0.5× 65 1.7k
Wojciech Macek Poland 27 1.1k 1.2× 143 0.3× 310 0.9× 810 2.9× 214 0.9× 96 1.6k
Onome Scott‐Emuakpor United States 18 781 0.8× 319 0.7× 233 0.7× 431 1.5× 209 0.9× 106 1.0k
Xiaofan He China 18 783 0.8× 179 0.4× 320 1.0× 361 1.3× 100 0.4× 53 1.1k
Yimin Zhang China 19 809 0.8× 164 0.4× 118 0.4× 256 0.9× 154 0.6× 105 1.2k
Shahed Rezaei Germany 22 353 0.4× 223 0.5× 296 0.9× 482 1.7× 151 0.6× 54 1.2k
Jyhwen Wang United States 21 1.0k 1.0× 246 0.5× 261 0.8× 538 1.9× 95 0.4× 84 1.3k
M.J. Roy United Kingdom 24 1.4k 1.5× 311 0.7× 289 0.9× 376 1.3× 45 0.2× 65 1.6k
Jwo Pan United States 19 1.1k 1.1× 479 1.1× 364 1.1× 666 2.4× 138 0.6× 113 1.7k

Countries citing papers authored by Ashley D. Spear

Since Specialization
Citations

This map shows the geographic impact of Ashley D. Spear'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 Ashley D. Spear with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashley D. Spear more than expected).

Fields of papers citing papers by Ashley D. Spear

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ashley D. Spear. 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 Ashley D. Spear. The network helps show where Ashley D. Spear may publish in the future.

Co-authorship network of co-authors of Ashley D. Spear

This figure shows the co-authorship network connecting the top 25 collaborators of Ashley D. Spear. A scholar is included among the top collaborators of Ashley D. Spear 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 Ashley D. Spear. Ashley D. Spear 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.
Spear, Ashley D., et al.. (2025). A deep learning framework to predict microstructurally small fatigue crack growth in three-dimensional polycrystals. Computer Methods in Applied Mechanics and Engineering. 437. 117689–117689.
2.
Logakannan, Krishna Prasath, Ibrahim Güven, Gregory M. Odegard, et al.. (2025). A review of artificial intelligence (AI)-based applications to nanocomposites. Composites Part A Applied Science and Manufacturing. 197. 109027–109027. 4 indexed citations
3.
He, Junyan, et al.. (2025). Predicting fall parameters from infant skull fractures using machine learning. Biomechanics and Modeling in Mechanobiology. 24(2). 521–537. 1 indexed citations
4.
Greeley, Duncan A., et al.. (2024). Quantitative analysis of three‐dimensional fatigue crack path selection in Mg alloy WE43 using high‐energy X‐ray diffraction microscopy. Fatigue & Fracture of Engineering Materials & Structures. 47(4). 1150–1171. 6 indexed citations
5.
Spear, Ashley D., et al.. (2024). Statistical analysis of microstructurally small fatigue crack growth in three-dimensional polycrystals based on high-fidelity numerical simulations. Engineering Fracture Mechanics. 307. 110282–110282. 2 indexed citations
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Spear, Ashley D., et al.. (2021). Computational analysis of the effects of geometric irregularities on the interaction of an additively manufactured 316L stainless steel stent and a coronary artery. Journal of the mechanical behavior of biomedical materials. 125. 104878–104878. 13 indexed citations
11.
Kouraytem, Nadia, et al.. (2020). Dynamic-loading behavior and anisotropic deformation of pre- and post-heat-treated IN718 fabricated by laser powder bed fusion. Additive manufacturing. 33. 101083–101083. 25 indexed citations
12.
He, Junyan, et al.. (2020). An adaptive-remeshing framework to predict impact-induced skull fracture in infants. Biomechanics and Modeling in Mechanobiology. 19(5). 1595–1605. 13 indexed citations
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Guilkey, James, et al.. (2019). A convected-particle tetrahedron interpolation technique in the material-point method for the mesoscale modeling of ceramics. Computational Mechanics. 64(3). 563–583. 8 indexed citations
16.
Spear, Ashley D., et al.. (2019). A voxel-based remeshing framework for the simulation of arbitrary three-dimensional crack growth in heterogeneous materials. Engineering Fracture Mechanics. 209. 404–422. 7 indexed citations
17.
Li, Xuxiao, Nadia Kouraytem, Vahid Tari, et al.. (2018). A multi-scale, multi-physics modeling framework to predict spatial variation of properties in additive-manufactured metals. Modelling and Simulation in Materials Science and Engineering. 27(2). 25009–25009. 53 indexed citations
18.
Spear, Ashley D., Surya R. Kalidindi, Bryce Meredig, Antonios Kontsos, & Jean‐Briac le Graverend. (2018). Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior. JOM. 70(7). 1143–1146. 27 indexed citations
19.
Cortina, Gerard, et al.. (2018). Generalized analytical displacement model for wind turbine towers under aerodynamic loading. Journal of Wind Engineering and Industrial Aerodynamics. 176. 120–130. 25 indexed citations
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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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