What is he best known for?
The fields of study he is best known for:
- Composite material
- Mechanical engineering
- Thermodynamics
His primary scientific interests are in Machining, Composite material, Mechanical engineering, Metallurgy and Grinding.
His biological study spans a wide range of topics, including Cutting tool, Diamond and Shear.
The concepts of his Composite material study are interwoven with issues in Crystallography and Morse potential.
His Mechanical engineering research incorporates elements of Instrumentation and Thermal, Thermal Problem.
The concepts of his Metallurgy study are interwoven with issues in Shear stress, Simple shear and Shear modulus.
As a member of one scientific family, he mostly works in the field of Grinding, focusing on Brittleness and, on occasion, Radius, Edge, Surface integrity and Process engineering.
His most cited work include:
- On the machining of fiber reinforced plastic (FRP) composite laminates (248 citations)
- Technological Advances in Fine Abrasive Processes (243 citations)
- Ultraprecision Metal Cutting — The Past, the Present and the Future (231 citations)
What are the main themes of his work throughout his whole career to date?
Ranga Komanduri spends much of his time researching Composite material, Machining, Polishing, Abrasive and Metallurgy.
His Composite material research is multidisciplinary, incorporating perspectives in Structural engineering and Silicon.
His work on Surface integrity as part of general Machining study is frequently linked to Chip formation, therefore connecting diverse disciplines of science.
His Abrasive research incorporates elements of Surface finish, Silicon nitride, Grinding, Work and Ceramic.
Ranga Komanduri studied Mechanical engineering and Thermal that intersect with Mechanics.
Ranga Komanduri focuses mostly in the field of Nanoindentation, narrowing it down to matters related to Finite element method and, in some cases, Inverse problem.
He most often published in these fields:
- Composite material (25.50%)
- Machining (22.82%)
- Polishing (14.77%)
What were the highlights of his more recent work (between 2009-2016)?
- Composite material (25.50%)
- Artificial intelligence (7.38%)
- Artificial neural network (6.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Composite material, Artificial intelligence, Artificial neural network, Chemical-mechanical planarization and Signal.
Composite material and Particle size are commonly linked in his work.
The various areas that he examines in his Artificial intelligence study include Bundle branch block and Pattern recognition.
His Chemical-mechanical planarization research includes themes of Wafer and Electronic engineering.
Ranga Komanduri combines subjects such as Asperity and Abrasive with his study of Diamond.
The study incorporates disciplines such as Surface and Sensor fusion in addition to Machining.
Between 2009 and 2016, his most popular works were:
- Determination of Mechanical Properties of Sand Grains by Nanoindentation (63 citations)
- Spatiotemporal representation of cardiac vectorcardiogram (VCG) signals (56 citations)
- Effect of Mass Density on the Compressive Behavior of Dry Sand Under Confinement at High Strain Rates (49 citations)
In his most recent research, the most cited papers focused on:
- Composite material
- Thermodynamics
- Mechanical engineering
His main research concerns Machining, Chemical-mechanical planarization, Signal, Composite material and Artificial intelligence.
The subject of his Machining research is within the realm of Mechanical engineering.
In his work, Surface roughness is strongly intertwined with Yield surface, which is a subfield of Mechanical engineering.
The Chemical-mechanical planarization study combines topics in areas such as Graph, Performance prediction, Electronic engineering and Theoretical computer science.
His Signal research incorporates themes from Supervised learning, Data mining and Naive Bayes classifier, Support vector machine.
His work on Representation and Feedforward neural network as part of general Artificial intelligence study is frequently linked to Configuration space and Reaction dynamics, bridging the gap between disciplines.
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