My recent research projects include...


A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects

ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE) 2016
IDETC 2016

A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects

The wide spread use of 3D acquisition devices with high- performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.

  • Authors: Erhan Batuhan Arisoy, Guannan Ren, Erva Ulu, Nurcan Gecer Ulu, Suraj Musuvathy
  • Year: 2016
  • Download: PDF
  • Publisher Version: www

Enhancing the Structural Performance of Additively Manufactured Objects Through Build Orientation Optimization

Journal of Mechanical Design, Special Issue on Design for Additive Manufacturing
JMD 2015

Enhancing the Structural Performance of Additively Manufactured Objects Through Build Orientation Optimization

Additively manufactured objects often exhibit directional dependencies in their structure due to the layered nature of the printing process. While this dependency has a significant impact the object’s functional performance, the problem of finding the best build orientation to maximize structural robustness remains largely unsolved. We introduce an optimization algorithm that addresses this issue by identifying the build orientation that maximizes the factor of safety of an input object under prescribed loading and boundary configurations. First, we conduct a minimal number of physical experiments to characterize the anisotropic material properties. Next, we use a surrogate-based optimization method to determine the build orientation that maximizes the minimum factor safety. The surrogate-based optimization starts with a small number of finite element solutions corresponding to different build orientations. The initial solutions are progressively improved with the addition of new solutions until the optimum orientation is computed. We demonstrate our method with physical experiments on various test models from different categories. We evaluate the advantages and limitations of our method by comparing the failure characteristics of parts printed in different orientations.

  • Authors: Erva Ulu, Emrullah Korkmaz, Kubilay Yay, O. Burak Ozdoganlar, Levent Burak Kara
  • Year: 2015
  • Download: PDF
  • Publisher Version: www

A Data-Driven Investigation and Estimation of Optimal Topologies Under Variable Loading Configurations

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. (Extended version of 2014 CompImage paper)
CMBBE 2015

A Data-Driven Investigation and Estimation of Optimal Topologies Under Variable Loading Configurations

Topology optimization problems involving structural mechanics are highly dependent on the design constraints and boundary conditions. Thus, even small alterations in such parameters require a new application of the optimization routine. To address this problem, we examine the use of known solutions for predicting optimal topologies under a new set of design constraints. In this context, we explore the feasibility and performance of a data-driven approach to structural topology optimization problems. Our approach takes as input a set of images representing optimal 2-D topologies, each resulting from a random loading configuration applied to a common boundary support condition. These images represented in a high dimensional feature space are projected into a lower dimensional space using component analysis. Using the resulting components, a mapping between the loading configurations and the optimal topologies is learned. From this mapping, we estimate the optimal topologies for novel loading configurations. The results indicate that when there is an underlying structure in the set of existing solutions, the proposed method can successfully predict the optimal topologies in novel loading configurations. In addition, the topologies predicted by the proposed method can be used as effective initial conditions for conventional topology optimization routines, resulting in substantial performance gains. We discuss the advantages and limitations of the presented approach and show its performance on a number of examples.

  • Authors: Erva Ulu, Rusheng Zhang, Levent Burak Kara
  • Year: 2015
  • Download: PDF
  • Publisher Version: www

A Data-Driven Investigation and Estimation of Optimal Topologies Under Variable Loading Configurations

CompIMAGE 2014
CompIMAGE 2014

A Data-Driven Investigation and Estimation of Optimal Topologies Under Variable Loading Configurations

We explore the feasibility and performance of a data-driven approach to topology optimization problems involving structural mechanics. Our approach takes as input a set of images representing optimal 2-D topologies, each resulting from a random loading configuration applied to a common boundary support condition. These images represented in a high dimensional feature space are projected into a lower dimensional space using component analysis. Using the resulting components, a mapping between the loading configurations and the optimal topologies is learned. From this mapping, we estimate the optimal topologies for novel loading configurations. The results indicate that when there is an underlying structure in the set of existing solutions, the proposed method can successfully predict the optimal topologies in novel loading configurations. In addition, the topologies predicted by the proposed method can be used as efective initial conditions for conventional topology optimization routines, resulting in substantial performance gains. We discuss the advantages and limitations of the presented approach and show its performance on a number of examples.

  • Authors: Erva Ulu, Rusheng Zhang, Mehmet Ersin Yumer, Levent Burak Kara
  • Year: 2014
  • Download: PDF
  • Publisher Version: www

Bio

I am currently a PhD candidate in the Mechanical Engineering Department at the Carnegie Mellon University. I am advised by Levent Burak Kara and work in the Visual Design and Engineering Lab.

My research interests include 3D printing, computer graphics, computational design and machine learning. Particularly, I work on algorithms that enhance the design process for fabrication methods.

Education

  • PhD Carnegie Mellon University, Mechanical Engineering (Current)
  • MSc Bilkent University, Mechanical Engineering (2012)
  • BS Middle East Technical University, Mechanical Engineering (2010)
  • About
    • Office: 5000 Forbes Avenue, Wean Hall 3412 Pittsburgh, PA 15213
    • Email: eulu@cmu.edu
    • LinkedIn: www
    • Résumé: PDF