What is he best known for?
The fields of study he is best known for:
- Composite material
- Mechanical engineering
- Electrical engineering
The scientist’s investigation covers issues in Nanotechnology, Tissue engineering, Machining, Mechanical engineering and Tool wear.
His Nanotechnology study integrates concerns from other disciplines, such as Laser, Mouse Fibroblast and Biofabrication.
Many of his research projects under Laser are closely connected to Biological materials with Biological materials, tying the diverse disciplines of science together.
His research in Tissue engineering intersects with topics in Inkjet printing and Polymer chemistry.
His study with Machining involves better knowledge in Metallurgy.
Yong Huang has researched Tool wear in several fields, including Grinding, Abrasion, Bearing and Boron nitride.
His most cited work include:
- Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations (433 citations)
- Laser-based direct-write techniques for cell printing. (212 citations)
- Scaffold‐free inkjet printing of three‐dimensional zigzag cellular tubes (203 citations)
What are the main themes of his work throughout his whole career to date?
Yong Huang mainly investigates Composite material, Nanotechnology, Machining, Laser and Metallurgy.
Yong Huang usually deals with Composite material and limits it to topics linked to Nozzle and Double layered and Voltage.
His work is dedicated to discovering how Nanotechnology, Tissue engineering are connected with Inkwell and other disciplines.
His Machining research is classified as research in Mechanical engineering.
His work on Fluence as part of general Laser study is frequently linked to Biological materials, bridging the gap between disciplines.
His studies examine the connections between Metallurgy and genetics, as well as such issues in Abrasion, with regards to Boron nitride and Stress.
He most often published in these fields:
- Composite material (29.24%)
- Nanotechnology (19.30%)
- Machining (15.79%)
What were the highlights of his more recent work (between 2017-2021)?
- Composite material (29.24%)
- Extrusion (7.60%)
- Nanotechnology (19.30%)
In recent papers he was focusing on the following fields of study:
Yong Huang focuses on Composite material, Extrusion, Nanotechnology, Tissue engineering and Inkwell.
His Composite material research is multidisciplinary, incorporating elements of Heat transfer and Machining.
Machining is a subfield of Metallurgy that he tackles.
His study in Nanotechnology is interdisciplinary in nature, drawing from both Three dimensional printing and Laser, Laser printing.
His Laser research is multidisciplinary, incorporating perspectives in Photonics and Transfer printing.
His Tissue engineering study improves the overall literature in Biomedical engineering.
Between 2017 and 2021, his most popular works were:
- Inverse determination of Johnson–Cook model constants of ultra-fine-grained titanium based on chip formation model and iterative gradient search (80 citations)
- Research needs and recommendations on environmental implications of additive manufacturing (73 citations)
- Interfacial bonding during multi-material fused deposition modeling (FDM) process due to inter-molecular diffusion (58 citations)
In his most recent research, the most cited papers focused on:
- Composite material
- Mechanical engineering
- Electrical engineering
His scientific interests lie mostly in Composite material, Extrusion, Nanotechnology, Machining and Nanocomposite.
Yong Huang undertakes interdisciplinary study in the fields of Composite material and Molecular diffusion through his works.
His Nanotechnology research includes elements of Three dimensional printing, Inkjet printing and Laser printing.
Chip formation is the focus of his Machining research.
His Chip formation research incorporates themes from Material properties, Viscoplasticity and Piezoelectricity.
His work in the fields of Nanocomposite hydrogels overlaps with other areas such as Distance effect and Direct printing.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
