T. D. Marusich

1.1k total citations · 1 hit paper
26 papers, 912 citations indexed

About

T. D. Marusich is a scholar working on Mechanical Engineering, Biomedical Engineering and Mechanics of Materials. According to data from OpenAlex, T. D. Marusich has authored 26 papers receiving a total of 912 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Mechanical Engineering, 18 papers in Biomedical Engineering and 11 papers in Mechanics of Materials. Recurrent topics in T. D. Marusich's work include Advanced machining processes and optimization (20 papers), Advanced Surface Polishing Techniques (18 papers) and Metallurgy and Material Forming (10 papers). T. D. Marusich is often cited by papers focused on Advanced machining processes and optimization (20 papers), Advanced Surface Polishing Techniques (18 papers) and Metallurgy and Material Forming (10 papers). T. D. Marusich collaborates with scholars based in United States. T. D. Marusich's co-authors include M. Ortíz, Shreyes N. Melkote, Rui Liu, Shuji Usui, Patxi Fernandez-Zelaia, M. Salahshoor, Raghuram V. Pucha, John Patten, Harish P. Cherukuri and Christopher J. Brand and has published in prestigious journals such as SHILAP Revista de lepidopterología, Materials Science and Engineering A and International Journal for Numerical Methods in Engineering.

In The Last Decade

T. D. Marusich

24 papers receiving 852 citations

Hit Papers

Modelling and simulation of high‐speed machining 1995 2026 2005 2015 1995 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
T. D. Marusich United States 13 779 510 287 246 177 26 912
H.‐W. Hoffmeister Germany 9 731 0.9× 556 1.1× 98 0.3× 120 0.5× 112 0.6× 21 814
Jens Sölter Germany 16 704 0.9× 421 0.8× 122 0.4× 137 0.6× 47 0.3× 58 760
Gérard Mauvoisin France 18 517 0.7× 351 0.7× 760 2.6× 285 1.2× 65 0.4× 55 965
H.S. Qi United Kingdom 10 495 0.6× 336 0.7× 146 0.5× 61 0.2× 64 0.4× 22 577
M.A. Davies United States 8 854 1.1× 644 1.3× 89 0.3× 132 0.5× 129 0.7× 18 935
Andreas Zabel Germany 13 420 0.5× 248 0.5× 71 0.2× 65 0.3× 66 0.4× 69 512
K.S. Neo Singapore 16 855 1.1× 742 1.5× 101 0.4× 158 0.6× 123 0.7× 20 991
G. Barrow United Kingdom 15 681 0.9× 384 0.8× 118 0.4× 86 0.3× 102 0.6× 24 723
Iqbal Shareef United States 11 467 0.6× 213 0.4× 271 0.9× 94 0.4× 30 0.2× 40 715
Marco Beghini Italy 20 949 1.2× 231 0.5× 763 2.7× 268 1.1× 154 0.9× 105 1.3k

Countries citing papers authored by T. D. Marusich

Since Specialization
Citations

This map shows the geographic impact of T. D. Marusich'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 T. D. Marusich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. D. Marusich more than expected).

Fields of papers citing papers by T. D. Marusich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. D. Marusich

This figure shows the co-authorship network connecting the top 25 collaborators of T. D. Marusich. A scholar is included among the top collaborators of T. D. Marusich 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 T. D. Marusich. T. D. Marusich 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.
Marusich, T. D., et al.. (2020). New Advances in the Machining of Hard Metals using Physics-Based Modeling. SHILAP Revista de lepidopterología. 61(1). 3–13.
2.
Fernandez-Zelaia, Patxi, Shreyes N. Melkote, T. D. Marusich, & Shuji Usui. (2017). A microstructure sensitive grain boundary sliding and slip based constitutive model for machining of Ti-6Al-4V. Mechanics of Materials. 109. 67–81. 32 indexed citations
3.
Melkote, Shreyes N., Rui Liu, Patxi Fernandez-Zelaia, & T. D. Marusich. (2015). A physically based constitutive model for simulation of segmented chip formation in orthogonal cutting of commercially pure titanium. CIRP Annals. 64(1). 65–68. 78 indexed citations
4.
Liu, Rui, M. Salahshoor, Shreyes N. Melkote, & T. D. Marusich. (2014). The Prediction of Machined Surface Hardness Using a New Physics-based Material Model. Procedia CIRP. 13. 249–256. 18 indexed citations
5.
Liu, Rui, M. Salahshoor, Shreyes N. Melkote, & T. D. Marusich. (2014). A unified material model including dislocation drag and its application to simulation of orthogonal cutting of OFHC Copper. Journal of Materials Processing Technology. 216. 328–338. 56 indexed citations
6.
Usui, Shuji, et al.. (2014). Finite Element Modeling of Carbon Fiber Composite Orthogonal Cutting and Drilling. Procedia CIRP. 14. 211–216. 37 indexed citations
7.
Usui, Shuji, et al.. (2013). A Gear Cutting Predictive Model Using the Finite Element Method. Procedia CIRP. 8. 51–56. 24 indexed citations
8.
Liu, Rui, et al.. (2013). An enhanced constitutive material model for machining of Ti–6Al–4V alloy. Journal of Materials Processing Technology. 213(12). 2238–2246. 58 indexed citations
9.
Liu, Rui, M. Salahshoor, Shreyes N. Melkote, & T. D. Marusich. (2013). A unified internal state variable material model for inelastic deformation and microstructure evolution in SS304. Materials Science and Engineering A. 594. 352–363. 26 indexed citations
10.
Usui, Shuji, et al.. (2013). A High Performance Computing Cloud Computing Environment for Machining Simulations. Procedia CIRP. 8. 57–62. 2 indexed citations
11.
Usui, Shuji, et al.. (2012). Validation of Finite Element Cutting Force Prediction for End Milling. Procedia CIRP. 1. 663–668. 37 indexed citations
12.
Marusich, T. D., et al.. (2012). Modeling of Cutting Forces and Cycle Times for Micromachined Components. Procedia CIRP. 1. 138–143. 4 indexed citations
13.
Usui, Shuji, et al.. (2011). Predictive Modeling for Distortion in Large, Thin-Walled Machined Components. Advanced materials research. 223. 56–65. 1 indexed citations
14.
Marusich, T. D., et al.. (2006). Residual Stress Prediction for Part Distortion Modeling. SAE technical papers on CD-ROM/SAE technical paper series. 1. 10 indexed citations
15.
Marusich, T. D., et al.. (2006). Three-Dimensional Finite Element Modeling of Drilling Processes. 471–478. 2 indexed citations
16.
Marusich, T. D., et al.. (2003). Three-Dimensional Finite Element Prediction of Machining-Induced Stresses. 1 indexed citations
17.
Marusich, T. D., et al.. (2003). Three-Dimensional Finite Element Modeling of Hard Turning Processes. 221–227. 1 indexed citations
18.
Cherukuri, Harish P., et al.. (2001). NUMERICAL SIMULATIONS OF DUCTILE MACHINING OF SILICON NITRIDE WITH A CUTTING TOOL OF DEFINED GEOMETRY. Machining Science and Technology. 5(3). 341–352. 28 indexed citations
19.
Marusich, T. D.. (2001). Effects of Friction and Cutting Speed on Cutting Force. 115–123. 36 indexed citations
20.
Marusich, T. D. & M. Ortíz. (1995). Modelling and simulation of high‐speed machining. International Journal for Numerical Methods in Engineering. 38(21). 3675–3694. 403 indexed citations breakdown →

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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