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Saigopal Nelaturi

dblp:00/7441 · DBLP profile ↗
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19ranked-venue papers
7as first author
4since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 19 · 7 first-author · 4 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
12 papers
Computational fabrication · 48% Geometric modeling and processing · 45% Visualization and visual analytics · 7%

Topics — the 10 heaviest of 11, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational fabrication
additive manufacturing
1.652022
Fabricated shape estimation for additive manufacturing processes with uncertainty · Comput. Aided Des. 2020
Automatic Support Removal for Additive Manufacturing Post Processing · Comput. Aided Des. 2019
A Classification of Topological Discrepancies in Additive Manufacturing · Comput. Aided Des. 2019
Geometric modeling and processing
topology optimization
1.022022
Topology Optimization for Manufacturing with Accessible Support Structures · Comput. Aided Des. 2022
Topology optimization with accessibility constraint for multi-axis machining · Comput. Aided Des. 2020
Geometric modeling and processing › shape analysis
shape estimation
0.412020
Fabricated shape estimation for additive manufacturing processes with uncertainty · Comput. Aided Des. 2020
Visualization and visual analytics › visualization design
design space exploration
0.412019
Exploring feasible design spaces for heterogeneous constraints · Comput. Aided Des. 2019
Computational fabrication › process planning
computer-aided process planning
0.312018
Automated process planning for hybrid manufacturing · Comput. Aided Des. 2018
Computational fabrication
hybrid manufacturing
0.312018
Automated process planning for hybrid manufacturing · Comput. Aided Des. 2018
Geometric modeling and processing
configuration space
0.322013
Solving inverse configuration space problems by adaptive sampling · Comput. Aided Des. 2013
Configuration products and quotients in geometric modeling · Comput. Aided Des. 2011
Computational fabrication
manufacturing planning
0.212014
Automated fixture configuration for rapid manufacturing planning · Comput. Aided Des. 2014
Computational fabrication › machining
multi-axis machining
0.112020
Topology optimization with accessibility constraint for multi-axis machining · Comput. Aided Des. 2020
Geometric modeling and processing
topology
0.112019
A Classification of Topological Discrepancies in Additive Manufacturing · Comput. Aided Des. 2019

Methods — techniques the papers use, named apart from their topics

accessible support structures · 0.6optimization · 0.4accessibility constraint · 0.4heterogeneous constraints · 0.4adaptive sampling · 0.2
YearPublicationVenuePosition
2022 SPM 2021 Editorial
Georges-Pierre Bonneau, Michael Barton 0002, Saigopal Nelaturi
Comput. Aided Des.3
2022 Topology Optimization for Manufacturing with Accessible Support Structures
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
Comput. Aided Des.3
2021 CAD Special Issue Editorial: Process Planning for Additive and Hybrid Manufacturing
Saigopal Nelaturi, Tsz-Ho Kwok
Comput. Aided Des.1
2021 Optimizing Build Orientation for Support Removal using Multi-Axis Machining
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
Comput. Graph.3
2020 Fabricated shape estimation for additive manufacturing processes with uncertainty
Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi
Comput. Aided Des.4
2020 SPM 2020 Editorial
Xin Li 0003, Michael Barton 0002, Saigopal Nelaturi
Comput. Aided Des.3
2020 Topology optimization with accessibility constraint for multi-axis machining
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
Comput. Aided Des.3
2020 Sliding Basis Optimization for Heterogeneous Material Design
Nurcan Gecer Ulu, Svyatoslav Korneev, Erva Ulu, Saigopal Nelaturi
Comput. Aided Des.4
2019 A Classification of Topological Discrepancies in Additive Manufacturing
Morad Behandish, Amir M. Mirzendehdel, Saigopal Nelaturi
Comput. Aided Des.3
2019 Exploring feasible design spaces for heterogeneous constraints
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
Comput. Aided Des.3
2019 Automatic Support Removal for Additive Manufacturing Post Processing
Saigopal Nelaturi, Morad Behandish, Amir M. Mirzendehdel, Johan de Kleer
Comput. Aided Des.1
2018 Automated process planning for hybrid manufacturing
Morad Behandish, Saigopal Nelaturi, Johan de Kleer
Comput. Aided Des.2
2015 Representation and analysis of additively manufactured parts
Saigopal Nelaturi, Vadim Shapiro
Comput. Aided Des.1
2014 Automated fixture configuration for rapid manufacturing planning
Saigopal Nelaturi, Arvind Rangarajan, Christian Fritz 0001, Tolga Kurtoglu
Comput. Aided Des.1
2013 Solving inverse configuration space problems by adaptive sampling
Saigopal Nelaturi, Vadim Shapiro
Comput. Aided Des.1
2011 Configuration products and quotients in geometric modeling
Saigopal Nelaturi, Vadim Shapiro
Comput. Aided Des.1
2011 Non-commutative morphology: Shapes, filters, and convolutions
Mikola Lysenko, Vadim Shapiro, Saigopal Nelaturi
Comput. Aided Geom. Des.3
2010 Group morphology with convolution algebras
abstract
Group morphology is an extension of mathematical morphology with classical Minkowski sum and difference operations generalized respectively to Minkowski product and quotient operations over arbitrary groups. We show that group morphology is a proper setting for unifying, formulating and solving a number of important problems, including translational and rotational configuration space problems, mechanism workspace computation, and symmetry detection. The proposed computational approach is based on group convolution algebras, which extend classical convolutions and the Fourier transform to non-commutative groups. In particular, we show that all Minkowski product and quotient operations may be represented implicitly as sublevel sets of the same real-valued convolution function.
Mikola Lysenko, Saigopal Nelaturi, Vadim Shapiro
Symposium on Solid and Physical Modeling2
2009 Configuration products in geometric modeling
abstract
The six-dimensional space SE(3) is traditionally associated with the space of configurations of a rigid solid (a subset of Euclidean three-dimensional space E3). But a solid can be also considered to be a set of configurations, and therefore a subset of SE(3). This observation removes the artificial distinction between shapes and their configurations, and allows formulation and solution of a large class of problems in mechanical design and manufacturing. In particular, the configuration product of two subsets of configuration space is the set of all configurations obtained when one of the sets is transformed by all configurations of the other. The usual definitions of various sweeps, Minkowski sum, and other motion related operations are then realized as projections of the configuration product into E3. Similarly, the dual operation of configuration quotient subsumes the more common operations of unsweep and Minkowski difference. We identify the formal properties of these operations that are instrumental in formulating and solving both direct and inverse problems in computer aided design and manufacturing. Finally, we show that all required computations may be implemented using a fast parallel sampling method on a GPU.
Saigopal Nelaturi, Vadim Shapiro
Symposium on Solid and Physical Modeling1