Davide Masato

1.0k total citations
55 papers, 785 citations indexed

About

Davide Masato is a scholar working on Mechanical Engineering, Automotive Engineering and Polymers and Plastics. According to data from OpenAlex, Davide Masato has authored 55 papers receiving a total of 785 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Mechanical Engineering, 27 papers in Automotive Engineering and 15 papers in Polymers and Plastics. Recurrent topics in Davide Masato's work include Injection Molding Process and Properties (40 papers), Additive Manufacturing and 3D Printing Technologies (27 papers) and Advanced machining processes and optimization (18 papers). Davide Masato is often cited by papers focused on Injection Molding Process and Properties (40 papers), Additive Manufacturing and 3D Printing Technologies (27 papers) and Advanced machining processes and optimization (18 papers). Davide Masato collaborates with scholars based in United States, Italy and United Kingdom. Davide Masato's co-authors include Giovanni Lucchetta, Marco Sorgato, Paolo Parenti, Massimiliano Annoni, David O. Kazmer, Ben Whiteside, Maksims Babenko, Simone Carmignato, Jitendra Singh Rathore and Leonardo Orazi and has published in prestigious journals such as Journal of Cleaner Production, Composites Science and Technology and Journal of Materials Processing Technology.

In The Last Decade

Davide Masato

52 papers receiving 762 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Davide Masato United States 17 544 319 211 162 97 55 785
Candice Majewski United Kingdom 15 461 0.8× 529 1.7× 181 0.9× 83 0.5× 194 2.0× 44 762
S. Berretta United Kingdom 10 342 0.6× 619 1.9× 309 1.5× 124 0.8× 132 1.4× 10 768
Mario Bragaglia Italy 16 310 0.6× 323 1.0× 174 0.8× 119 0.7× 68 0.7× 51 760
Vincent Sobotka France 17 631 1.2× 281 0.9× 93 0.4× 254 1.6× 140 1.4× 59 906
Carlos H. Ahrens Brazil 17 474 0.9× 507 1.6× 164 0.8× 51 0.3× 186 1.9× 43 789
Tomasz Kozior Poland 19 378 0.7× 661 2.1× 270 1.3× 68 0.4× 242 2.5× 59 850
Min‐Young Lyu South Korea 15 361 0.7× 171 0.5× 142 0.7× 292 1.8× 74 0.8× 91 760
A. S. Pouzada Portugal 16 469 0.9× 239 0.7× 202 1.0× 311 1.9× 106 1.1× 85 824
Federica Trovalusci Italy 13 251 0.5× 138 0.4× 128 0.6× 75 0.5× 49 0.5× 62 518
Ernst Schmachtenberg Germany 12 286 0.5× 288 0.9× 213 1.0× 198 1.2× 69 0.7× 32 737

Countries citing papers authored by Davide Masato

Since Specialization
Citations

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

Fields of papers citing papers by Davide Masato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Davide Masato. 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 Davide Masato. The network helps show where Davide Masato may publish in the future.

Co-authorship network of co-authors of Davide Masato

This figure shows the co-authorship network connecting the top 25 collaborators of Davide Masato. A scholar is included among the top collaborators of Davide Masato 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 Davide Masato. Davide Masato 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
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Ma, Ruizhe, et al.. (2025). Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene. Polymers. 17(7). 940–940. 1 indexed citations
4.
Masato, Davide, et al.. (2025). Experimental Measurement and Multivariate Modeling of Thermal Contact Resistance Under Injection Molding Conditions. Polymer Engineering and Science. 65(9). 4882–4895.
5.
Sobkowicz, Margaret J., et al.. (2024). Analysis of the Embodied Energy of Different Grades of Injection-Molded Polypropylene. Journal of Manufacturing and Materials Processing. 8(4). 182–182. 1 indexed citations
6.
Bowen, N.S., et al.. (2024). Mechanical and crystallization properties of hot runner injection molded virgin and recycled polypropylene. Polymer Engineering and Science. 64(5). 2241–2255. 7 indexed citations
7.
Masato, Davide, et al.. (2024). The Effects of Nucleating Agents and Processing on the Crystallization and Mechanical Properties of Polylactic Acid: A Review. Micromachines. 15(6). 776–776. 16 indexed citations
8.
O’Meara, Sarah, et al.. (2024). In‐mold rheology and automated process control for injection molding of recycled polypropylene. Polymer Engineering and Science. 64(9). 4112–4127. 8 indexed citations
9.
Masato, Davide & Sun Kyoung Kim. (2023). Global Workforce Challenges for the Mold Making and Engineering Industry. Sustainability. 16(1). 346–346. 7 indexed citations
10.
Masato, Davide, et al.. (2023). Thermoformability analysis of TPO sheets blended with postindustrial secondary feedstock. Polymer Engineering and Science. 63(7). 1932–1942. 4 indexed citations
11.
Sobkowicz, Margaret J., et al.. (2023). Thermo-mechanical recycling via ultrahigh-speed extrusion of film-grade recycled LDPE and injection molding. Sustainable materials and technologies. 38. e00719–e00719. 10 indexed citations
12.
Masato, Davide, David O. Kazmer, & Andrea Gruber. (2023). Meta‐analysis of thermal contact resistance in injection molding: A comprehensive literature review and multivariate modeling. Polymer Engineering and Science. 63(12). 3923–3937. 6 indexed citations
13.
Sobkowicz, Margaret J., et al.. (2023). Investigation of pressure-controlled injection molding on the mechanical properties and embodied energy of recycled high-density polyethylene. Sustainable materials and technologies. 36. e00651–e00651. 14 indexed citations
14.
Kazmer, David O., et al.. (2023). Strategic cost and sustainability analyses of injection molding and material extrusion additive manufacturing. Polymer Engineering and Science. 63(3). 943–958. 30 indexed citations
15.
Mackey, Lauren Grace, et al.. (2023). Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding. Micromachines. 14(4). 863–863. 10 indexed citations
16.
Sobkowicz, Margaret J., et al.. (2023). Investigation of Pressure-Controlled Injection Molding on the Mechanical Properties and Embodied Energy of Recycled Hdpe. SSRN Electronic Journal. 2 indexed citations
17.
Brown, Eric, et al.. (2020). Hybrid Process Chain for the Integration of Direct Ink Writing and Polymer Injection Molding. Micromachines. 11(5). 509–509. 12 indexed citations
18.
Masato, Davide, Marco Sorgato, Afif Batal, Stefan Dimov, & Giovanni Lucchetta. (2019). Thin‐wall injection molding of polypropylene using molds with different laser‐induced periodic surface structures. Polymer Engineering and Science. 59(9). 1889–1896. 15 indexed citations
19.
Masato, Davide, Marco Sorgato, & Giovanni Lucchetta. (2018). Effect of ultrasound vibration on the ejection friction in microinjection molding. The International Journal of Advanced Manufacturing Technology. 8 indexed citations
20.
Masato, Davide, Marco Sorgato, Paolo Parenti, Massimiliano Annoni, & Giovanni Lucchetta. (2016). Impact of Micro Milling Strategy on the Demolding Forces in Micro Injection Molding. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 167–170. 1 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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